Systems and methods for addressable targeting of advertising content

ABSTRACT

A method of targeting of advertising content for a consumer product is disclosed. The method comprises obtaining consumer demographic data from a first server over a network, the consumer demographic data including a plurality of demographic attributes for each person among a plurality of persons; obtaining product purchaser data for a plurality of product purchasers of the consumer product from a second server over the network, each product purchaser among the plurality of product purchasers being among the plurality of persons; and enriching the purchaser data with the consumer demographic data. The method further comprises enriching viewing data with consumer demographic data; and selecting viewed media among the aggregated viewed media having the highest similarity to the product purchasers as target media for the advertising content.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority to U.S. ProvisionalPatent Application No. 62/032,965, entitled “Systems and Methods forAddressable Targeting of Advertising Content,” filed on Aug. 4, 2014,which is incorporated herein by reference in its entirety.

This application also makes reference to U.S. Nonprovisional applicationSer. No. 13/209,346, entitled “Automatically Targeting Ads to TelevisionUsing Demographic Similarity,” filed Aug. 12, 2011, which isincorporated herein by reference in its entirety.

TECHNICAL FIELD

The present disclosure relates to systems and methods for evaluatingtelevision media instances for advertisement spots based on variousfactors for reaching television viewers who are desired product buyers.

BACKGROUND

Television is very different from online advertising. In onlineadvertising, it is possible to deliver ads to individual persons. Intelevision, advertisements have traditionally been embedded in a singlehigh definition video stream and broadcast using over-the-airterrestrial transmission towers, satellite, and cable.

However those traditional limitations with television are beginning todisappear. Due to new and better set top boxes, several cable operatorsand satellite providers have begun to allow advertisers to direct theirads to individuals. In the television advertising industry, this isreferred to as “addressable targeting,” and refers to delivering an adto a specific household, which then sits on the set top box and triggersbased on specific conditions.

Current systems supporting some addressable capabilities include Dishand DirecTV using the Invidi Set Top Box. Cablevision is capable ofaddressable advertising on 3.5 million Motorola, Cisco and Pace set topboxes in the New York market; and Comcast has announced addressablecapabilities that work on Video On Demand using BlackArrow and their X1Set Top Box.

Although addressable capabilities are beginning to emerge, this has beena very slow process, and the industry has a long history of hyping thetechnology and then finding little adoption. Several problems areholding addressable television advertising back. First, there is a lackof targeting algorithms that will work on television infrastructure.Namely, it is one thing to have the hardware to target ads toindividuals, but the advertiser still needs to know to whom to delivertheir ads. The targeting algorithm needs to be able to be able to workwith the relatively low subscriber counts that many cable operatorshandle (the TV industry is quite diverse, so there are cable operatorswho have only a few million subscribers. A direct match between thesesubscribers and an advertiser's database will result in very fewmatches). Another problem is that a market design is needed so thattelevision addressable inventory can be bought and sold in an efficientmanner. Finally, there is a desire for a way for the advertiser toestimate the value from targeting addressable inventory. From theseller's point of view, there needs to be a way to rationally setprices.

The present disclosure is directed to overcoming one or more of theseabove-referenced challenges.

SUMMARY OF THE DISCLOSURE

According to certain embodiments, a method is disclosed for targeting ofadvertising content for a consumer product, the method comprising:obtaining, from a first server over a network, product purchaser datafor a plurality of product purchasers of the consumer product;obtaining, from a second server over the network, cable subscriber datafor a plurality of cable subscribers; calculating by a hardwareprocessor a first similarity between one or more product purchasersamong the plurality of product purchasers and one or more cablesubscribers among the plurality of cable subscribers; and selectingcable subscribers among the plurality of cable subscribers having thehighest first similarity to the product purchasers as target cablesubscribers for the advertising content.

According to certain embodiments, a system is disclosed for targeting ofadvertising content for a consumer product, the system comprising: afirst server providing product purchaser data for a plurality of productpurchasers of the consumer product over a network; a second serverproviding cable subscriber data for a plurality of cable subscribersover the network; an advertising targeting controller configured to:obtain the product purchaser data and the cable subscriber data;calculate by a hardware processor a first similarity between one or moreproduct purchasers among the plurality of product purchasers and one ormore cable subscribers among the plurality of cable subscribers; andselect cable subscribers among the plurality of cable subscribers havingthe highest first similarity to the product purchasers as target cablesubscribers for the advertising content.

According to certain embodiments, a non-transitory computer readablemedium storing a program causing a computer to execute a method oftargeting of advertising content for a consumer product is disclosed,the executed method comprising: obtaining, from a first server over anetwork, product purchaser data for a plurality of product purchasers ofthe consumer product; obtaining, from a second server over the network,cable subscriber data for a plurality of cable subscribers; calculatingby a hardware processor a first similarity between one or more productpurchasers among the plurality of product purchasers and one or morecable subscribers among the plurality of cable subscribers; andselecting cable subscribers among the plurality of cable subscribershaving the highest first similarity to the product purchasers as targetcable subscribers for the advertising content.

Additional objects and advantages of the disclosed embodiments will beset forth in part in the description that follows, and in part will beapparent from the description, or may be learned by practice of thedisclosed embodiments. The objects and advantages of the disclosedembodiments will be realized and attained by means of the elements andcombinations particularly pointed out in the appended claims. As will beapparent from the embodiments below, an advantage to the disclosedsystems and methods is that multiple parties may fully utilize theirdata without allowing others to have direct access to raw data. Thedisclosed systems and methods discussed below may allow advertisers tounderstand users' online behaviors through the indirect use of raw dataand may maintain privacy of the users and the data.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1 depicts an exemplary analytics environment and an exemplarysystem infrastructure for modeling and detailed targeting of televisionmedia, according to exemplary embodiments of the present disclosure.

FIG. 2 depicts a flowchart for high dimensional set top box targeting,according to exemplary embodiments of the present disclosure.

FIGS. 3A and 3B depict an addressable targeting algorithm process,according to exemplary embodiments of the present disclosure.

FIG. 4 depicts a schematic diagram of detailed demographic matchstatistics on a particular TV program and its suitability foradvertising, for example, a handyman product, according to exemplaryembodiments of the present disclosure.

FIG. 5 depicts inputs and outputs for individual targeting using mediasimilarity, according to exemplary embodiments of the presentdisclosure.

FIG. 6 depicts a flowchart of an exemplary method for individualtargeting using media similarity, according to exemplary embodiments ofthe present disclosure.

FIG. 7 depicts a flowchart of an exemplary method for individualaddressable targeting using demographic similarity (labeled herein as“Algorithm D”), according to exemplary embodiments of the presentdisclosure.

FIG. 8 depicts a graphical representation of sample demographics for anexemplary advertiser, according to exemplary embodiments of the presentdisclosure.

FIG. 9 depicts a graphical representation of addressable targeting scoreversus expected revenue from customers for an exemplary advertiser,according to exemplary embodiments of the present disclosure.

FIG. 10 depicts a graphical representation of addressable targetingscore versus the time that a policy has been held by targeted personsfor an exemplary advertiser, according to exemplary embodiments of thepresent disclosure.

FIG. 11 depicts a graphical representation of cumulative distributionfor buyers per asset from three different targeting algorithms,according to exemplary embodiments of the present disclosure.

FIG. 12 depicts a graphical representation of addressable targetingalgorithm performance as buyers per impression versus tratio, accordingto exemplary embodiments of the present disclosure.

FIG. 13 depicts a graphical representation of addressable lift versus %of assets targeted, reported in percentiles, according to exemplaryembodiments of the present disclosure.

FIG. 14 depicts a graphical representation of buyers per impressionratio between addressable lift and M32 or TRP lift, according toexemplary embodiments of the present disclosure.

FIG. 15 depicts a flowchart of an exemplary method for an addressablemarket design, according to exemplary embodiments of the presentdisclosure.

FIG. 16 depicts a sample screenshot of an exemplary set of buyablemedia, according to exemplary embodiments of the present disclosure.

FIG. 17 depicts a sample screenshot of an exemplary set of buyablemedia, according to exemplary embodiments of the present disclosure.

FIG. 18 depicts a sample screenshot of an exemplary set of buyablemedia, according to exemplary embodiments of the present disclosure.

DETAILED DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an exemplary analytics environment and an exemplarysystem infrastructure for modeling and detailed targeting of televisionmedia, according to exemplary embodiments of the present disclosure.

FIG. 2 depicts a flowchart for high dimensional set top box targeting,according to exemplary embodiments of the present disclosure.

FIG. 3 shows high-level data flows for one embodiment of AddressableAlgorithm D. Cable subscribers and Product purchasers are bothanonymized via a “clean room,” and demographics are added to bothpopulations. A product purchaser demographic profile is then generatedby aggregating the product purchaser population, where-as the cablesubscriber population remains non-aggregated—each cable subscriber willbe scored against the overall product purchaser profile. Then for eachproduct-purchaser profile and cable subscriber profile, the demographicversions of the product purchasers and cable subscribers can then becompared. Finally a score is calculated based on the quality of matchbetween product purchasers and cable subscribers. Each cable subscribermay have a score generated indicating how well they match thedemographics of the product purchasers.

FIG. 4 depicts the demographics of product purchasers compared to thedemographics of a specific cable subscriber. The more closely do thedemographics of product purchasers match the cable subscriber, thebetter is the cable subscriber for addressable advertising. In oneembodiment, cable subscriber demographics may each be 0-1 variableswhere 0 means that they do not have the demographic trait, and 1 meansthat they do have the demographic trait. However these binary scores arethen normalized by the rarity of the demographic, giving rise to thereal-valued values shown in this figure.

FIG. 5 depicts a very broad view of the inputs and outputs for theaddressable targeting algorithms (Algorithm C and D) described in thisdisclosure. Given an advertiser's ad (not shown), the system takeshistorical buyers of the advertiser's product 110 and a new cablesubscriber population 120, and scores the cable subscriber populationfor targeting. These scores may be estimates of the probability of buyerPr(Buyer) or a similar score for propensity to purchase the advertiser'sproduct 130, which may represent the probability of the cable subscriberin question being a potential buyer of the advertiser's product.Advertisers would generally desire to target higher Pr(Buyer) cablesubscribers as they are more likely to receive the advertiser'smessaging and potentially purchase their product.

FIG. 6 depicts a flowchart for Algorithm C described in the presentdisclosure. Algorithm C is an addressable algorithm which uses viewingbehavior to score cable subscribers against a product purchaserpopulation.

FIG. 7 depicts a flowchart of an exemplary method for addressabletargeting using demographic similarity (labeled herein as “AlgorithmD”).

FIG. 8 depicts some of the top variables from a demographic profilegenerated from a population of purchasers. The demographics shown inthis figure have been normalized to z-scores. In z-scores, a positivevalue means that the trait occurs more than that of a referencepopulation such as US pop. This shows that for this particular purchaserpopulation (they happen to be life insurance purchasers), they tend tohave cholesterol interest, are retired or pensioners, African American,and have incomes <$15,000 per year. When we use this profile to findcable subscribers, one of the methods described (Algorithm D) measuresthe demographic vector match between this purchaser profile and thecable subscriber demographics—thus we should find cable subscribers whoare also African American, have low incomes, are retired, and havecholesterol interest. The z-scores used here take the original traitrate of occurrence (e.g. a percentage such as 20% let's say forcholesterol interest) and converted into a standardized score bysubtracting the typical rate for US population e.g. let's say about 5%)and then dividing by the standard deviation of trait occurrence asmeasured in the US population.

FIG. 9 shows lift analysis of an addressable targeting algorithm. Avariety of current, former, and potential life insurance customers wereused and each had both revenue accrued from their policies and “Monthsin force” as the number of months they had retained their life insurancepolicy. The population—for whom we knew their value—was then scored inthe same way that we would score cable subscribers. They were thenordered by tratio. Their expected policy durations and revenue were thenshown by tratio. The result showed that as targeting score increases, sodoes the expected revenue. The ratio of expected value at a given tratioand expected value for population is the lift potential due toaddressable targeting algorithm. This provides an estimate of lift thatthe algorithm is likely to achieve when executed in a real addressabletelevision campaign, and a means for estimating a cost-effective CPM forthe addressable campaign.

FIG. 10 is similar to FIG. 9, but shows results against 3 sub-clustersfor the same advertiser. Different sub-clusters could each havedifferent value, and this shows up clearly in this graph. The differentcustomer value, in turn, changes the calculation of expected value dueto the use of the addressable targeting algorithm.

FIG. 11 shows what kind of performance a marketer could expect if theytarget different amounts of assets. This figure was calculated using theBuyer per million approach of estimating lift potential—where we measurethe buyer per million concentration in the cable subscriber populationthat would be targeted under different algorithms. An optimal algorithmwould hug the left-hand axis and the diagonal line indicates randomperformance. For example, if the top scoring 1% of the population weretargeted using Addressable targeting, the lift compared to random/massmarket ads would be 9.9×. If the top 2% of subscribers were targeted,the lift would drop to 6.5×. The diagonal line shows the performance ofa theoretical campaign in which assets are bought randomly.

FIG. 12 depicts a graphical representation of addressable targetingalgorithm performance as buyers per impression versus tratio, accordingto exemplary embodiments of the present disclosure.

FIG. 13 depicts a graphical representation of addressable lift versus %of assets targeted, reported in percentiles, according to exemplaryembodiments of the present disclosure.

FIG. 14 depicts a graphical representation of buyers per impressionratio between addressable lift and M32 or TRP lift, according toexemplary embodiments of the present disclosure.

FIG. 15 depicts a flowchart of an exemplary method for an addressablemarket design, according to exemplary embodiments of the presentdisclosure.

FIG. 16 depicts a zoom out in an audience planner GUI according toexemplary embodiments of the present disclosure showing the differentassets or packages that could be purchased, bucketed by tratio. Thex-axis is the tratio. Each square in the column of squares may representa buyable asset or package that has a targeting score in the tratiobucket being displayed. The higher tratio assets often can include morewebsites and digital segments, as it may be possible to get a more“pure” target audience with these assets, although they may also be muchsmaller than TV programs. TV programs may occupy many of the lowertratio buckets.

FIG. 17 shows a zoom in of FIG. 16 with more detail. The Zoom in makesit easier to see the different icons being used to represent each asset.For example, in this particular embodiment, the “WWW” icon represents awebsite, and the “People” icon represents a digital segment. “Insp”represents the “Inspiration network”—a television network that runsreligious programming. Addressable audiences are represented by a “Playbutton” icon.

FIG. 18 shows a set of buyable media including TV program 1810, digitalsegment 1830, website 1820, and “Insertable VOD” (1840). An advertisercan insert their ad to a commercial break in the TV program, can havetheir ad display to online persons who are members of a specific digitalsegment, can have their ad run on a particular website, or can run theirad—for example—in the pre-roll of a video on demand movie. “InsertableVOD (Insertable Video On Demand) 16% of Pop” (1840) is a viewer packagethat can be purchased that will capture the top 16% of population whoare most likely to convert on the advertiser's product. Grouping thepersons into a buyable “package” makes it easier for an advertiser tobid or buy the addressable product.

DETAILED DESCRIPTION OF EMBODIMENTS

Aspects of the present disclosure, as described herein, relate tosystems and methods for automated television ad targeting using set topbox data. Aspects of the present disclosure involve selecting a segmentof TV media to purchase to insert an ad, such that advertiser value perdollar is maximized.

Various examples of the present disclosure will now be described. Thefollowing description provides specific details for a thoroughunderstanding and enabling description of these examples. One skilled inthe relevant art will understand, however, that the present disclosuremay be practiced without many of these details. Likewise, one skilled inthe relevant art will also understand that the present disclosure mayinclude many other related features not described in detail herein.Additionally, some understood structures or functions may not be shownor described in detail below, so as to avoid unnecessarily obscuring therelevant description.

The terminology used below may be interpreted in its broadest reasonablemanner, even though it is being used in conjunction with a detaileddescription of certain specific examples of the present disclosure.Indeed, certain terms may even be emphasized below; however, anyterminology intended to be interpreted in any restricted manner will beovertly and specifically defined as such in this Detailed Descriptionsection.

The systems and method of the present disclosure allow for automatedtelevision ad targeting using set top box data.

I. System Architecture

Any suitable system infrastructure may be put into place to place toreceive media related data to develop a model for targeted advertisingfor television media. FIG. 1 and the following discussion provide abrief, general description of a suitable computing environment in whichthe present disclosure may be implemented. In one embodiment, any of thedisclosed systems, methods, and/or graphical user interfaces may beexecuted by or implemented by a computing system consistent with orsimilar to that depicted in FIG. 1, which may operate according to thedescriptions of U.S. patent application Ser. No. 13/209,346, filed Aug.12, 2011, the disclosure of which is hereby incorporated herein byreference. Although not required, aspects of the present disclosure aredescribed in the context of computer-executable instructions, such asroutines executed by a data processing device, e.g., a server computer,wireless device, and/or personal computer. Those skilled in the relevantart will appreciate that aspects of the present disclosure can bepracticed with other communications, data processing, or computer systemconfigurations, including: Internet appliances, hand-held devices(including personal digital assistants (“PDAs”)), wearable computers,all manner of cellular or mobile phones (including Voice over IP(“VoIP”) phones), dumb terminals, media players, gaming devices,multi-processor systems, microprocessor-based or programmable consumerelectronics, set-top boxes, network PCs, mini-computers, mainframecomputers, and the like. Indeed, the terms “computer,” “server,” and thelike, are generally used interchangeably herein, and refer to any of theabove devices and systems, as well as any data processor.

Aspects of the present disclosure may be embodied in a special purposecomputer and/or data processor that is specifically programmed,configured, and/or constructed to perform one or more of thecomputer-executable instructions explained in detail herein. Whileaspects of the present disclosure, such as certain functions, aredescribed as being performed exclusively on a single device, the presentdisclosure may also be practiced in distributed environments wherefunctions or modules are shared among disparate processing devices,which are linked through a communications network, such as a Local AreaNetwork (“LAN”), Wide Area Network (“WAN”), and/or the Internet. In adistributed computing environment, program modules may be located inboth local and remote memory storage devices.

Aspects of the present disclosure may be stored and/or distributed onnon-transitory computer-readable media, including magnetically oroptically readable computer discs, hard-wired or preprogrammed chips(e.g., EEPROM semiconductor chips), nanotechnology memory, biologicalmemory, or other data storage media. Alternatively, computer implementedinstructions, data structures, screen displays, and other data underaspects of the present disclosure may be distributed over the Internetand/or over other networks (including wireless networks), on apropagated signal on a propagation medium (e.g., an electromagneticwave(s), a sound wave, etc.) over a period of time, and/or they may beprovided on any analog or digital network (packet switched, circuitswitched, or other scheme).

II. The TV Ad Targeting Problem

A. Television Media

According to various embodiments of the present disclosure, a TV MediaInstance Mi (also known as a “spot”) may be used to reference a segmentof time on television which can be purchased for advertising. A mediainstance Mi may be defined as an element of the Cartesian product of thefollowing:

M _(i) ∈S×P×D×H×T×G×POD×POS×L  [Equation 1]

where S is Station, P is Program, D is Day-Of-Week, H is Hour-Of-Day, Tis Calendar-Time, G is Geography, POD is the Ad-Pod, POS is thePod-Position, and L is Media-Length. Stations may include Broadcast andCable stations and may be generally identified by their call-letters,such as KIRO and CNN. Geography may include National, Direct MarketAssociation Areas, such as Miami, Fla. and Cable Zones, such as ComcastMiami Beach. An “Ad Pod” may be a term used to reference a set ofadvertisements that run contiguously in time during the commercial breakfor a TV program. “Pod position” may be a term used to reference thesequential order of the ad within its pod. “Media Length” may be a termused to reference the duration of the time segment in seconds—common adlengths include 30, 15, and 60 second spots.

Media may be bought in rotations, which may be subsets of the abovemedia instances, where some of the asset is a “wildcard.” For example, amedia buyer could buy a Network-Day-Hour of CNN-Tues-8 pm, withoutspecifying Pod, and could run it over several weeks. According toNielsen Competitive Data, there are over 20 million TV ad mediainstances per month in the United States which an advertiser can targetwith their ad.

B. Addressable Television Media

A single buyable unit of Addressable TV inventory can be defined aseither (1) a cable/satellite/television subscriber to target with asingle ad exposure:

M _(i) ∈PER  [Equation 2-0]

or (2) a combination of media insertion and person, as described belowin Equation 2, which means that the TV ad targeting problem becomes oneof determining which persons to target, and during which program, day,hour, and pod position.

M _(i) ⁺ ∈M _(i) ×PER  [Equation 2]

Person, or PER in Equation 2-0 or 2, typically refers to an individualset top box device—in general television systems don't know exactlywhich person in a household is viewing at any time. Often the cablesubscriber's billing name and address is used as a proxy for person, andother members of the household are able to be added as potential viewersfrom the demographic enrichment process.

Delivery of the ad with addressable television systems is another areawhere there can be some differences from system to system. Currentaddressable TV systems often have the ad cached on the viewer's set topbox, and when they watch television, they overlay the ad over a standardtelevision spot. Some addressable systems place the ad in places otherthan standard advertising pods, such as on navigation screens or as apre-roll to video on demand content. However, conventional ads could besold into such positions as well. Because of this we will regard thesewithin-video-stream pre-rolls, navigation placements, etc. as all beingpossible placements of our previous definition Mi.

Finally, we have so far talked about targeting an individual unit ofaddressable media—usually a person. It is possible to also define a setof this inventory, which we could call a “package” Package={Mi+}. In thetelevision advertising industry, “Packages” are often the name given totraditional television media where ads are bundled into differentprograms. However here we will find the term useful for describing abuyable block of population possibly including media placementspecifications. We will discuss this more in a later section when wetalk about the operation of a market for addressable inventory.

Embodiments of the present disclosure focus methods for targeting anddelivering to probable buyers.

C. Example Objective

In one embodiment, the ad targeting problem for the advertiser is toselect a set of one or more media {Mi} such that the expected number ofbuyers reached per impression is maximized, per dollar spent onadvertising:

$\begin{matrix}{M_{i}^{+}\text{:}\mspace{14mu} \max \frac{r_{\Omega}\left( \left\{ M_{i}^{+} \right\} \right)}{{CPM}\left( \left\{ M_{i}^{+} \right\} \right)}} & \left\lbrack {{Equation}\mspace{14mu} 3} \right\rbrack\end{matrix}$

where r_(Ω)({M_(i) ⁺}) are the buyers per impression viewing the media“package” and CPM({M_(i) ⁺}) is the cost per thousand persons who wouldbe delivered the addressable advertisement. The price CPM({M_(i) ⁺}) foraddressable inventory is often available from the network as “ratecards” or list prices for selling their addressable inventory. A sellerof addressable inventory could also set CPM({M_(i) ⁺}) dynamically usingthe calculation of r_(n)({M_(i) ⁺})—the more valuable are theaddressable persons, the higher can be the price for those persons. Theunknown, however, is the intrinsic lift that is possible usingaddressable versus media targeting in terms of buyers reached. Thus, theproblem we will initially focus on is estimating r_(Ω)({M_(i) ⁺}) andspecifically, finding a set of persons that have very high values forr_(Ω)({M_(i) ⁺}) for the advertiser in question. There are severalmethods for estimating r_(Ω)({M_(i) ⁺}), and we will now describe someof these techniques for conventional media as well as addressable media.

III. Spot Television Targeting Approaches

i. Target Rating Points (“Algorithm A”)

Target Rating Points (TRPs) on age-gender demographics are a traditionalmethod for targeting conventional television spots. This form oftargeting defines a “Target Rating Point” as the number of persons whomatch the advertiser's target demographics divided by total populationin a targeted area. In order to convert this into a measure ofprecision, it may be expressed as number of persons who match theadvertiser's demographics divided by total viewing persons andmultiplied by 100. Therefore, 100 means that of the people watching aparticular program, all of them were the desired target. It is common touse this technique on Nielsen reported age and gender counts.

In one embodiment, where P _(d,v) may be defined as the demographics ofthe set of persons who the advertiser wishes to target. A demographicvalue p_(d,v)∈{0, 1, MV} may be defined to be a formal proposition aboutthe person p of the form d=v, e.g., income=$50K . . . $60K. Theproposition p_(d,v) equals 1 if it is true, 0 if false, and missingvalue (MV) if it is unknown. Q(Mi) may be defined as a set of viewerswho are watching TV media instance Mi and where this viewing activity isrecorded by the Nielsen panel and qk∈Q(Mi) where # may be defined as thecardinality of a set, and # r_(T) may be defined as persons that matchon all demographics, then the TRPs for Media Instance Mi can be definedas follows:

$\begin{matrix}{{r_{\Omega}\left( M_{i}^{+} \right)} = {{{TRP}\left( {P,M_{i}} \right)} = {{r\left( {P,M_{i}} \right)} = {100 \cdot \frac{\# {r_{T}\left( {M_{i},P} \right)}}{\# {Q\left( M_{i} \right)}}}}}} & \left\lbrack {{Equation}\mspace{14mu} 4} \right\rbrack\end{matrix}$

where q_(j)∈r_(T)(M_(v) P) if ∀d,v:q_(j,d,v)=P _(d,v) and no values canbe missing. For example 50 means that 50% of the people are a match tothe desired demographics.

Age and gender demographics are used widely for target rating points.This begs the question of why other demographics (e.g. income, number ofchildren, interest in fishing, purchaser-of-petite-apparel) aren't alsoused. A close analysis of the targeting formula suggests that it appearsto be quite problematic to use a larger number of rich demographicdescriptions. One issue is panel size: Nielsen's panel only has 25,000people distributed across 210 Direct Marketing Association areas, soabout 119 people per area. There have been media reports of major ratingshifts due to a single African American panelist moving, which ispossible given that on average there would only be 16. African Americansper area. As a result, rare demographics may well have too few personsto be usable, where age and gender may be the only demographics exposedbecause they are the only demographics with enough data to be reliable.

Another issue is treating the demographics as predicates has an adverseeffect on the amount of media that meets the user's criteria. Even witha broad age-gender combination such as Male 25-34, the subset that meetsthat definition is only 1.8% of the full population. Therefore in aprogram with 100,000 viewers, 1,800 would match the target at random.The subset shrinks even further if an advertiser attempts to targetusing more demographics. With 3,000 demographics specified, almost nopeople will have the exact same demographic readings that the advertiseris trying to reach, and so the method will routinely report 0% in thetarget group or statistically unreliable numbers.

In summary, because the TRP measure uses Boolean expressions where atarget is in or out—this tends to mean that additional variables used todescribe the population exponentially decrease the population that is“in-target.” Every additional descriptor cuts down the pool oftargetable people, making it very hard to use any more than 2 or 3demographics in practice.

The root problem is there is no concept of “similarity” in the TargetRating Points scheme—for example, 35 year old females are similar to 34year old females, yet the 35 year olds are outside of the 25-34 target.Ideally it would be possible to use thousands of demographics to helpdescribe a target. The Algorithms that are described next use similaritybased schemes for matching media rather than Boolean expressions.

ii. High Dimensional Set Top Box Targeting (“Algorithm B” or “M32”)

We now describe an algorithm that addresses some of the limitationsinherent with the TRP algorithm, and works on traditional televisionmedia. The algorithm is illustrated in FIG. 2.

In one embodiment, P may be defined as a set of persons who havepurchased an advertiser's product. (210). p_(j)∈P is a person in the setto be targeted.

Commercially available consumer demographics may then be obtained (205)in order to enrich each of the product purchaser persons with D=3,000demographics. Let each demographic trait be represented as a 0-1variable, where 0 means the person doesn't have the trait, and 1 meansthey do have it. We can then define a demographic vector for each personp as p_(j,d,v)∈{0, 1, MV} (215)

Let # P_(d) be the cardinality of the set of persons who have thedemographic d with any value v that is non-missing. Calculate for eachdemographic d,v—as the probability of a demographic proposition d=vbeing true in the advertiser's set of purchasers (225).

$\begin{matrix}{{\overset{\_}{P}}_{d,v} = {\frac{1}{\# P_{d}}\Sigma_{p_{j} \in P}p_{j,d,v}}} & \left\lbrack {{Equation}\mspace{14mu} 5} \right\rbrack\end{matrix}$

We can regard P as a desirable demographic probability vector. Thisprofile can now be targeted on TV media according to embodiments of thepresent disclosure.

The system next obtains viewing activity from set top box persons (230)and enriches the set top box viewing persons with the same D=3000demographics (235).

An example set of Set Top Box viewing activity is shown in Table 1.Table 1 includes actual Set Top Box viewing record for exemplary Person10195589 showing station, program, and date. The demographics for thisviewer include “Male,” “Owns SUV”, “Age=44-45”, “Interest in spectatorsports,” “motorcycle racing,” “football,” “baseball,” and “basketball.”

TABLE 1 An example set of preson-level set top box records with fields(Network (StationCallLetters), Person (PersonKey), DateTime, ViewMinutes(Mins), Program (ProgramName)) Network Person DateTime Mins Program ESPN10195589 3/10/2012 15:00 22 College Basketball SCIFI 10195589 3/10/201215:00 7 Survivorman SCIFI 10195589 3/10/2012 15:30 4 Survivorman ESPN10195589 3/10/2012 15:30 26 College Basketball ESPN 10195589 3/10/201216:00 30 College Basketball ESPN 10195589 3/10/2012 17:30 12 CollegeBasketball ESP2 10195589 3/10/2012 17:30 17 NASCAR Racing ESP2 101955893/10/2012 18:00 30 NASCAR Racing ESP2 10195589 3/10/2012 18:30 7 NASCARRacing ESPN 10195589 3/10/2012 18:30 2 College Basketball SCIFI 101955893/10/2012 18:30 21 Survivorman ESPN 10195589 3/10/2012 19:00 3 CollegeBasketball ESP2 10195589 3/10/2012 19:00 22 NASCAR Racing ESP2 101955893/10/2012 19:30 12 NASCAR Racing NICK 10195589 3/10/2012 19:30 29Victorious ESPN 10195589 3/10/2012 19:30 18 College Basketball NICK10195589 3/10/2012 20:00 9 Big Time Movie

Embodiments of the present disclosure may then aggregate each piece ofmedia Mi (e.g., Survivorman 3:00 pm, 3/10/2012 in Table 1) into anidentically sized D-dimensional demographic vector M _(i) based on theset of persons who viewed that television program (240) as shown below:

$\begin{matrix}{{\overset{\_}{M}}_{i,d,v} = {\frac{1}{\# {Q_{d}\left( M_{i} \right)}}\Sigma_{q_{k} \in {Q{(M_{i})}}}q_{k,d,v}}} & \left\lbrack {{Equation}\mspace{14mu} 6} \right\rbrack\end{matrix}$

A similarity or “tratio” r between advertiser target P and media M _(i)may be defined as the correlation coefficient between the product andmedia demographic vectors (245).

$\begin{matrix}{{{tratio}\left( {\overset{\_}{P},{\overset{\_}{M}}_{i}} \right)} = {{r\left( {\overset{\_}{P},{\overset{\_}{M}}_{i}} \right)} = \frac{{\overset{\_}{P}}^{+} \cdot {\overset{\_}{M}}_{i}^{+}}{{{\overset{\_}{P}}^{+}} \cdot {{\overset{\_}{M}}_{i}^{+}}}}} & \left\lbrack {{Equation}\mspace{14mu} 7} \right\rbrack \\{{{\overset{\_}{P}}_{D,V}^{+} = \frac{{\overset{\_}{P}}_{d,v} - \mu_{d,v}}{\sigma_{d,v}}};{{\overset{\_}{M}}_{i,d,v}^{+} = \frac{{\overset{\_}{M}}_{i,d,v} - \mu_{d,v}}{\sigma_{d,v}}}} & \left\lbrack {{Equation}\mspace{14mu} 8} \right\rbrack\end{matrix}$

and are the mean and standard deviation of the demographic from anunbiased US population. Embodiments of the present disclosure mayexclude any demographics (convert them to missing) if they have fewerthan B=25 people.

IV. Addressable Targeting Algorithms

Addressable television targeting differs from conventional TV mediatargeting in that it is scoring cable subscribers rather than programs.The problem for an addressable targeting algorithm, as illustrated inFIG. 5, is to take historical buyers 110 and a new cable subscriberpopulation 120, and to score the cable subscriber population fortargeting with the advertising as Pr(Buyer) 130, the probability ofbeing a buyer.

We will now describe two embodiments for addressable targeting:

A. Individual Addressable Targeting Using Media Similarity (“AlgorithmC”)

One approach illustrated in FIG. 6 is to decompose persons into a vectorof network-program viewing propensities. Embodiments of the presentdisclosure may find the set of programs watched by buyers (620) and thenmay find cable subscribers who over-index on those same programs (630),and these may be selected as candidates to target (640). This may be agood option for TV Cable Operators because they may use their own settop box viewing data to drive the match possibly without any third partydata being required.

Embodiments of the present disclosure may implement such an algorithmaccording to the following pseudo-code:

Pseudo-code for Algorithm C

1. Let P equal the set of cable subscribers who could be targeted usingthe addressable ad delivery systems.

2. Let B equal the set of buyers who have purchased an advertiser'sproduct or service. This set of buyers is usually provided by theadvertiser. It is also possible to define a “proxy target” which is aset of persons identified by some algorithm (e.g. high-income 20 yearolds). We need a set of people as a “seed target.”

3. For each cable subscriber p∈P, calculate their viewing minutes as apercentage of time spent on each station-program versus all viewingminutes for the cable subscriber. This comprises the followingsub-steps:

3a. Canonicalize Program Names: “The Walking Dead” and “The Walking DeadMon” may both appear as program names in a television schedule. Thelatter might refer to the “Monday encore” of the premiere Walking Deadepisode that airs on Sunday night. However the different strings used inthe program name unfortunately fragment the viewing behavior. This makesit more difficult to get a clear signal around cable subscriber viewingpreferences. Therefore it may be canonicalized into a “mastered” versionof the program name, “The Walking Dead.” Canonicalization can beperformed using a lookup table that maps the different string forms ofthe program to a standardized string. Table C shows an example of thecanonicalization table. Program MasterID in Table C is a unique “ProgramMaster” identifier for the canonicalized program name.ExternalProgramTitle refers to string variants that map to the canonicalversion.

TABLE C Example from Mapping Program Name Variations table 3b. GenerateSet Top Box data with (PersonKey, DateTimeStart, Station, Program,Viewing Minutes). Program Master Id External Program Title 154 TheWalking Dead 154 Walking Dead 154 Walking Dead Marathon 154 Walking DeadEnc

This entails the following steps:

3b1. Obtain raw Set Top Box channel change event data on cablesubscriber viewing events. This data generally comprises a record suchas (PersonKey, DateTime, ChannelViewed). The PersonKey is actually amapping from the Set Top Box DeviceID to the household (usuallyrepresented as the cable subscriber's name and address, although thespecific personally identifiable information is usually converted intoan anonymousID to protect personal privacy). Throughout our descriptionswe usually refer to Personkeys, however we note that Householdlds wouldalso work for many of the applications described. It is also possible toapply filters to the Households, for example, only using households with1 device, so as to increase the signal strength, or filtering outhouseholds that have >x devices. For example, households with six ormore devices could be hotels, and for these kinds of households, theviewing activity of the known cable subscriber may have littlerelationship to the viewing data for the entirety of the household.Filtering down the set of households being used can help to increasesignal strength for vector matching.

TABLE D Example data from Households Table. This table maps householdsto personkeys. Personkeys are anonymized person name-addresses and canbe equal to the cable subscriber who is paying for the service. MarketUpdate Household Id Person Key Master Id Zipcode Date 1 10236545 27518431 Sep. 1, 2011 2 10241750 275 18071 Sep. 1, 2011 3 10266571 27518445 Jan. 27, 2012 4 10228206 76 16912 Jan. 27, 2012 5 10238532 19318053 Sep. 1, 2011 6 10275284 275 18322 Sep. 1, 2011 7 10236807 27518466 Sep. 1, 2011 8 14560233 275 18466 Jan. 27, 2012 9 10179306 27518466 Sep. 1, 2011 10 10211165 76 16912 Sep. 1, 2011

3b2. Using the Zipcode from the address that we have for the PersonKey,and a look-up to the television schedule running on that day usingStation and DateTime, process the above raw record into (PersonKey, Bin(DateTime), StationMasterID, ProgramMasterId, MarketMasterId). Note thatProgram Name is derivable from Program MasterID, and CallLetters (orsometimes called Station) are derivable from StationMasterID.MarketMasterID is also derived from Zipcode and represents thegeographic broadcast area (Direct Marketing Area) where the person islocated. Local broadcast television stations operate in different areas,and so this makes it possible to lookup the correct local station givenviewing activity on a national network such as ABC.

TABLE E HouseholdViewingHistory Table Station Program Market Bin(DateTime) Person Key Master ID Master Id Master Id 10/2/2013 0:002081102 710 13 9 10/2/2013 1:30 2081102 710 685 9 10/2/2013 2:00 2081102710 685 9 10/2/2013 2:30 2081102 710 685 9 10/2/2013 3:00 2081102 71074845 9 10/2/2013 3:30 2081102 710 74845 9 10/2/2013 4:00 2081102 710651 9 10/2/2013 4:30 2081102 710 651 9 10/2/2013 5:00 2081102 710 5412 910/2/2013 5:30 2081102 710 7851 9

3b3. Sessionize the Set Top Box viewing events: Sessionization involvessorting the events by PersonKey, DateTime, and then cutting a session ifthere is no activity for more than INACTIVITY_TIME hours. We tend to useINACTIVITY_TIME=4 hours as our sessionization time. Cutting the sessionmeans that the personkey's viewing is assumed to end, so we put aceiling on the viewminutes for the preceding Station-Program in thatcable subscriber's viewing events.

3b4. Delete Channel change events: Channel change events occur whenviewers are navigating through different channels, or flipping channels,and are alighting on a given channel for less thanCHANNEL_CHANGE_DURATION seconds. We find that most channel change eventsoccur with a 5 second time or lower, but we have also found that we canuse a channel-change threshold of CHANNEL_CHANGE_DURATION=30 secondsgives good results in practice.

3b4. After sessionizing, we then calculate the time in seconds viewedbetween subsequent programs. This is calculated by taking the differencein timestamp between subsequent program viewing events. We then output:(PersonKey, DateTime, Station Callletters, Program Name, ViewSeconds);and example of this is shown in Table F.

TABLE F PersonViewingHistory Table Person Station View Key Date TimeCallletters Program Name Seconds 13417536 03/28/2012 12:00 WTNZ JudgeMathis 3000 13327781 03/20/2012 9:00 DSNY Morning 1800 1336124403/11/012 20:30 853 NBA Basketball 1800 13330603 03/31/2012 20:00 HBONever Let Me Go 1800 13360182 03/24/2012 20:00 TCM Movie 180 1332083703/25/2012 22:00 MSNB Caught on 180 Camera 13360182 03/21/2012 21:00 A&EDog the Bounty 3600 Hunter 13289535 03/22/2012 20:00 SPK ImpactWrestling 1140 13394656 03/14/2012 18:00 USA NCIS 1560 1336701703/14/2012 3:00 LMN Ordinary Miracles 300

3c. Calculate Station Program viewing percentages by cable subscriber:For each PersonKey, we sum all viewseconds for each Station Program, andthen divide by the total viewseconds for that PersonKey. The output is atable personviewing_profile=(PersonKey, Station Callletters, ProgramName, ViewSecondsPct). Table G shows an example of this output.

TABLE G SetTopBox.PersonStationProgramValue Table Station View SecondsPerson Key Callletters Program Name Pct 4 AMC The Green Mile 0.12811855210 SYFY The Twilight Zone 0.204059109 28 FNEW FOX & Friends 0.09090909132 HISI The Revolution 0.104868336 36 TBS The Big Bang Theory0.139690037 42 NKJR Yo Gabba Gabba! 0.133822306 44 HLN Morning Expresswith 0.117905882 Robin Meade 57 NGC Earth: The Biography 0.19161565 59GRN A Haunting 0.16832462 65 DISC A Haunting 0.143390965

4. Given the set of Buyers B, anonymously match the Buyers against thecable subscriber population P. Let b=Intersection(B,P) be the set ofpersons who are both cable subscribers, and are buyers; we will callthese product purchaser—subscribers or buyer-cable subscribers.Calculation of the match can be done by using one of several methoddescribed below:

4a. One method is to use a universal identifier that represents persons,where the same universal identifier is assigned to the productpurchasers as well as the cable subscribers. An algorithmic method toaccomplish this is a process (often a third party because it creates alayer of privacy, but does not have to be) takes name-addressinformation and attaches an anonymous Identifier to person records, andthen strips the personally identifiable information and sends it back.The same service is then used for both product purchasers, and cablesubscribers. Thus we then have a set of product purchasers with auniversal identifier, a set of cable subscribers with universalidentifier, and then we can select out cable subscribers who are exactmatches to product purchasers. Companies including Acxiom and Experiancurrently offer an anonymization and universalID tagging service thatworks as above.

4b. There may be other processes which estimate that cable subscribersare similar to the product purchasers, for example demographics could beused to find cable subscribers who are similar to the product purchasersbased on demographics, and these could be regarded as the“pseudo-buyer-cable-subscribers” for the purposes of the presentalgorithm.

6. For all persons who are buyer-cable-subscribers b, calculate anoverall ViewSecondsPct for each Station Program for this group ofpeople. This can be done by summing all Station, Program, viewsecondsfor persons and then dividing by total viewseconds for the group. Theoutput is a table that we can call buyer_profile=(Station, Program,ViewSecondsPct). An example of this table is shown in table H.

TABLE H SetTopBox.StationProgramValue Table. This table holds the “buyerprofile” of programs that buyers tend to watch and the percentage oftime viewing each program. Station View Seconds Callletters Program NamePct AMC The Man From Snowy River 0.005071 DISC Jack the Ripper inAmerica 0.009774 DISC Brazil Butt Lift 0.003043 GOLF Best of MorningDrive 0.009656 DISC Track Me if You Can 0.010092 BLOM Countdown WithOwen Thomas and 0.001463 Linzie Janis AMC The Hills Have Eyes 0.00243WNBC NFL Football Preseason 0.024068 AMC Support Your Local Gunfighter0.013499 AMC Halloween III: Season of the Witch 0.009682

7. For all persons in the cable subscriber population who are notbuyers, i.e. Candidate=P−B, measure the similarity between their viewpercentage vector (Table G) and the buyer view percentage vector (TableH). This can be calculated in several ways, but one method is belowwhere P ⁺ represents the view vector for buyer-cable subscriber, and M_(i) ⁺ the view vector for the non-buyer cable subscribers.

$\begin{matrix}{{r_{E}\left( {\overset{\_}{P},{\overset{\_}{M}}_{i}} \right)} = \frac{{\overset{\_}{P}}^{+} \cdot {\overset{\_}{M}}_{i}^{+}}{{{\overset{\_}{P}}^{+}} \cdot {{\overset{\_}{M}}_{i}^{+}}}} & \left\lbrack {{Equation}\mspace{14mu} 81} \right\rbrack\end{matrix}$

This produces a table like that shown in Table I. Table A also shows a10 line snippet from the algorithm output table with more detailincluding some of the additional statistics that can be generated. Forexample, Table A can also show many programs matched between non-buyercable subscriber and the buyer profile.

TABLE I Excerpt from Person.TargetTRatio Table for illustrationpurposes. A more complete version can be found in Table A. Person KeyTRatio 10174988 0.24898 10174989 −0.12579 10174990 −0.22265 10174991−0.4316 10174992 −0.23603 10174993 −0.35799 10174994 0.159995 101749950.167286 10174996 −0.36491 10174997 −0.44456

The highest tratio persons in the list above could be considered thebest prospects for an addressable targeting campaign, in terms of theirraw probability of being purchasers and other factors not taken intoaccount. This raw targeting score can be combined with otherinformation—such as the number of times that these individuals havereceived the advertising message previously (user-specific frequency),and the cost per thousand for reaching subsets of these customers (oftenthe larger is the group of cable subscribers which is being activelytargeted, the lower will be the cost per thousand price for thosesubscribers) when determining which cable subscribers or package ofcable subscribers to target.

A key practical advantage of this particular algorithm is that it doesnot need to use third party demographics data in order to accomplishaddressable targeting. This can reduce data and processing cost, speedup the time between receiving a request for targeting and being able toprovide back a package of addressable candidates, and enables data to besecured within a smaller number of entities.

B. Individual Addressable Targeting Using Demographic Similarity(“Algorithm D” or “ADDR”)

We will now describe an addressable television algorithm that uses analternative method to score cable subscribers. This algorithm usesdemographics to calculate degree-of-match to the product purchasers.FIG. 3 shows this algorithm graphically.

Embodiments of the present disclosure may use demographics to calculatethe match between target product purchasers and cable subscribers. Asshown in FIGS. 3, 5 and 7, embodiments may obtain consumer demographicdata (705), product purchaser data (710), and cable subscriber data(720). The demographic data may be used to enrich each of the historicalbuyers (715) and the cable subscribers (725), and may then be used togenerate a target demographic profile. That demographic profile may thenbe matched to other cable subscribers (730) to find persons who have theclosest demographics (735). The identified persons and may be reportedas cable subscribers with the closest match to ideal as possibleadvertising targets (740), such as according to Equation 10, forexample.

$\begin{matrix}{{r_{E}\left( {\overset{\_}{P},{\overset{\_}{M}}_{i}} \right)} = \frac{{\overset{\_}{P}}^{+} \cdot {\overset{\_}{M}}_{i}^{+}}{{{\overset{\_}{P}}^{+}} \cdot {{\overset{\_}{M}}_{i}^{+}}}} & \left\lbrack {{Equation}\mspace{14mu} 9} \right\rbrack\end{matrix}$

We will now provide a detailed walkthrough of this algorithm.Embodiments of the present disclosure may implement such an algorithmaccording to the following pseudo-code:

Pseudo-Code for Algorithm D:

The algorithm will take as inputs a Cable SubscriberPopulation-To-Be-Scored, and a Product Purchaser Target Definition. Bothof these inputs are represented as “source keys” by the system (TableA2, A3 and Table B)—which is a unique identifier that is used assignedto particular populations of persons—or to the aggregated demographicresults from those populations (it is technically possible to have asourcekey that represents just the demographic vector without anunderlying population—for example, it could be hand-specified. In such acase as this there is still a sourcekey representing the unique targetand virtual population to which it represents).

The output from the algorithm will be each cable subscriber person inthe Population-To-Be-Scored with a tratio which measures the matchbetween their demographics and the target's demographics (Table Acontains the raw algorithm output and Table B contains the “permanentstorage.” Interestingly Table B is both an output and an input—thiscontains the persons to be scored, and it also holds the tratio outputafter scoring).

We will define several tables that we will use for our algorithm:

The Person table assigns each anonymized person in the database aPersonKey. An example of this table is shown in Table A1.

Each person is linkable to either an advertiser or cable company'srepresentation of a person, which we term a “customer” in our databaseschema. CustomerKey in our schema captures the native key used for theperson—this is a foreign key which makes it possible to track any personprocessed by the system, back to the originating record that wasprovided to it.

UniversalID is another optional field which makes it possible to matchthe anonymous person in different contexts. For example, productpurchasers and cable subscribers could both have UniversalIDs, and anexact match means that we have the same person.

TABLE A1 Person Person State/ Zip/ Univer- Key Name City ProvincePostalCode salID 1 1 Greeley CO 80634 A 2 25 Cottontown TN 37048 B 3 32COLUMBIA SC 29209 C 4 411 Pine Hill NJ 08021 D 5 51 ALMIRA WA 99103 E 66333 MONTICELLO KY 42633 F 7 74 FALL CREEK WI 54742 G 8 82 PHILADELPHIAPA 19111 H 9 91 MILLEN GA 30442 I 10 10 Beaumont TX 77706 J

Each person belongs to a sourcekey, which means a collection orpopulation of a company's customers. The sourcekey is defined in theSource table and the definition of which source a person is mapped to isdefined in the PersonSource table. Table A2 and A3 show sources andpersonsource.

TABLE A2 Source Table with a selection of example columns. TheSourceType field shows that we can define sources for a number ofpurposes - for example, sources could be product purchaser populations(eg. “Customers”), a 1% sample of US Population (“1% Sample”),look-a-like populations (“STB Lookalike”), Response targets -populations inferred from direct response data using another process(“Response targets”) and so on. Source Source Company Source KeyDescription Project Key Name Source Type Create Date 110572 a 10134 aCustomer Subset 3/25/2015 10:08 AM 110573 b 10134 b Customer Subset3/26/2015 2:11 PM 110574 c 10134 c Customers 4/2/2015 10:40 AM 110575 d10150 d Response Target 5/6/2015 10:30 AM 110576 e 10155 e Customers5/6/2015 11:25 AM 110577 f 10155 f Customer Subset 5/8/2015 10:15 AM110578 g 10155 g Customer Subset 5/8/2015 10:18 AM 110579 h 10155 hCustomer Subset 5/8/2015 10:23 AM 110580 i 10155 i Customer Subset5/8/2015 10:25 AM 110586 j 10134 j Response Target 6/19/2015 2:47 PM110587 k 10159 k Customers 7/21/2015 3:34 PM 110588 l 10164 l 1% Sample7/23/2015 1:42 PM 110589 m 10164 m 1% Sample 7/23/2015 1:42 PM 110590 n10167 n Customers 7/28/2015 3:57 PM 110591 o 10165 o Customers 7/28/20153:57 PM 110496 p 10092 p STB Lookalike 3/21/2014 10:53 AM 110497 q 10093q STB Lookalike 3/21/2014 11:05 AM 110087 r 110087 r 1% Sample 1/1/201012:00 AM 110076 s 110076 s 1% Sample 1/1/2010 12:00 AM

TABLE A3 Simplified example of PersonSource to illustrate the concept:Each person can belong to multiple sourcekeys. Table B shows anotherexample of this table but with more columns. Source Key Person Key 400 1400 3 400 5 400 6 400 7 400 8 400 9 400 10 400 11 400 12

Each person also has a mapping to a set of demographic attributes whichmay have been generated by an enrichment process with a third partycompany that specializes in demographics. The demographics attributedetails are stored in two tables: Demographics and DemographicsValue.The Demographics table contains demographic attribute such as “age,”“gender,” “income,” and so on. The DemographicsValue table stores thespecific sub-values for that demographic, such as “age=18to20, age=21 .. . 24” and so on. Table A4 and A5 shows the demographics anddemographics value tables.

TABLE A4 Demographics Table DemographicsID Demographics Name 23 AllergyRelated Interest 24 Arthritis, Mobility Interest 25 Health - CholesterolFocus 26 Diabetic Interest 27 Health - Disabled Interest 28 OrthopedicInterest 29 Senior Needs Interest 30 PC Internet Connection Type 31Single Parent 32 Veteran 33 Occupation - Professional

TABLE A5 DemographicsValue Demographics Value ID Demographics IDDemographics Value Name 91 30 Cable Internet 92 30 DSL Internet 93 30Dial-Up Internet 96 33 Occupation - Professional 97 33 Architect 98 33Chemist 99 33 Curator 100 33 Engineer 101 33 Aerospace Engineer 102 33Chemical Engineer

The mapping between a PersonKey and DemographicsValueID are stored inthe PersonDemographicsMap table. This table generally contains a recordor row for a demographic variable-value trait, only when it is“present,” meaning that this table only stores 1s in terms ofdemographics. 0s are not stored and are implicitly assumed to be 0, andso absence of a trait is inferred to mean that there is a 0 for thatdemographic. This improves storage significantly.

In the equations we have defined demographic variable values asvariables such as x, d, v where x is the person, d is a demographic, andv is a value. The x, d, v variables are defined by thePersonDemographicsMap table.

Although we often refer to the combination of demographic withdemographicvalue, Demographicsvalueid can be set up to function as theprimary key and can uniquely describe the specific combination ofdemographic and demographicvalue.

TABLE A6 PersonDemographicsMap Table. This table only shows 0-1demographic variable-value traits that are “present” (i.e. 1) for agiven person. Person Key Demographics ID Demographics Value ID 1017498814 76 10174988 44 55953 10174988 47 539 10174988 58 55972 10174988 63660 10174988 65 662 10174988 84 681 10174988 101 695 10174988 116 71210174988 134 764

Some examples of cable subscriber person demographic profiles for anexample Personkey=19048092 are shown in table J, K, L, and A7 below.

TABLE J Personkey = 19048092 has the above demographic traits relatingto “Marital status.” Note that the z-score versions of the traits arealso shown. This is what is used for vector matching. In the abovetable, “Demographics Source Desc” refers to the demographic-demographicvalue value represented as a string for human readability,“has demo” means that the person has this demo (in this case “MaritalStatus”) and “has variable value” indicates that they have a particularvariable- value (in this case “Married”). The above table says thatx(MaritalStatus, Married) = 1. Demographics has variable Source Desczscore value has demo Single −15.03442585 0 1 Inferred Single−3.702081749 0 1 Inferred Married −2.015544526 0 1 Married 9.63324338 11

TABLE K Personkey = 19048092 has the above demographic traits relatingto “Income - Narrow ranges.” Note that the z-score versions of thetraits are also shown. This is what is used for vector matching. In theabove table, “Demographics Source Desc” refers to the demographic-demographicvalue value represented as a string for human readability,“has demo” means that the person has this demo (in this case “IncomeNarrow Ranges”) and “has variable value” indicates that they have aparticular variable-value (in this case “$125,000- $149,999”). The abovetable says that x(IncomeNarrowRanges, $125,000-$149,000) = 1.Demographics has variable Source Desc zscore value has demo Less than$15,000 −1.732861144 0 1 $15,000-$19,999 −2.05088993 0 1 $20,000-$29,999−3.127608118 0 1 $30,000-$39,999 −7.338503177 0 1 $50,000-$59,999−14.19369231 0 1 $70,000-$79,999 −7.407445639 0 1 $80,000-$89,999−3.478227705 0 1 $90,000-$99,999 −0.755536358 0 1 $100,000-$124,999−2.835779486 0 1 $125,000-$149,999 21.66825797 1 1 Greater than $149,999−1.949531257 0 1

TABLE L Personkey = 19048092 has the following top and bottomdemographic traits. The above demographics are a special kind ofdemographic called an “indicator” demographic - these only have a valueof 1 or if missing are inferred to be 0. Thus all of these areequivalent to saving “Demographic = true”. The above table shows topnegative traits also - these indicate that it is more unusual to nothave these traits compared to the US Population - for example, it isunusual to not be interested in SpectatorSports - Football, and so thez-score for this demographic trait (which is a 0 for this customer) ismore negative than the others. has variable Demographic name Zscorevalue has demo Exercise-Aerobic 79.92347757 1 1 Career 70.03063336 1 1High-TechLiving 68.84047148 1 1 Fishing 68.64658538 1 1 Boating/Sailing68.55685224 1 1 Travel-International 64.76999726 1 1 Photography62.71386773 1 1 Travel-C 62.22263895 1 1 AutoWork −1.448205109 0 0HomeFurnishings/Decorating −1.449296904 0 0 HomeLiving −1.478087135 0 0Homeimprovement −1.513269184 0 0 BroaderLiving −1.525012494 0 0Dieting/WeightLoss −1.543605543 0 0 Camping/Hiking −1.602009909 0 0SpectatorSports-Football −1.783328723 0 0

Each cable subscriber person X may have a set of demographics x={0,1}that may be set to 0 or 1. Rather than storing 0 demographics, thedatabase schema can be set up to only show demographics that are“present” or 1. That will decrease the amount of storage in thedemographic table. Let that demographic vector be X where xd,v={0,1}.Table A7 shows an example of person demographic, demograpihcvalues,along with a z-score to represent the “unusualness” of this valuecompared to the US population.

TABLE A7 PersonVariableValueProfile Demographics Person Key DemographicsID Value ID ZScore 10174988 93 56025 0.683761948 10174988 93 560260.968439159 10174988 93 56027 0.204178975 10174988 93 56028 0.8054148510174988 93 56029 −0.208246564 10174988 93 56030 0.62596653 10174988 9356031 −0.038819315 10174988 93 56032 0.11029697 10174988 94 560330.013483197 10174988 94 56034 0.267608507

Each advertiser target Y may be an “idealized” demographic vector whichmay define the demographic vector that they are trying to obtain. Theadvertiser target itself is defined as a “sourcekey” and itself may havea population of persons that collectively together define the target.These persons are product purchasers who have bought the advertiser'sproduct before.

Let the demographic vector for the product purchasers be Y whereyd,v=(0,1); ie. each yd,v is a probability. The probabilities for thisvector may be calculated by taking the total persons with thedemographic trait divided by the total persons, or alternatively, theprobabilities may be hand-entered or picked up from a different system.

The overall zscore demographic profile for the target product purchasersis stored in the VariableValueProfile table (Table A8). Table L showsthe table in more detail, including details on the number of personsbehind each statistic.

TABLE A8 SourceVariableValueProfile Demographics Source Key DemographicsID Value ID ZScore 10021 93 56025 0.683761948 10021 93 56026 0.96843915910021 93 56027 0.204178975 10021 93 56028 0.80541485 10021 93 56029−0.208246564 10021 93 56030 0.62596653 10021 93 56031 −0.038819315 1002193 56032 0.11029697 10021 94 56033 0.013483197 10021 94 560340.267608507

TABLE L1 columns 1-6: Centroid customers with variable refers to thenumber of persons in US Population who have the demographic. Centroidcustomers with variable value refers to the number of persons in USpopulation who have the variable-value. Using these we create a Centroidvariable value pct of variable (Table L1 columns 7-14). The table alsocontains source variable value pct of variable - this is thecorresponding percent of the time that this demographic variable-valueis 1 in the population. The source and centroid versions of thisvariable can be compared to create a z-score which measures theunusualness of the demographic trait compared to its reference or“centroid” population (usually the US population). Centroid Centroidcustomers Source Demo- Demo- customers with segment graphics graphicswith variable key id value id ZScore variable value 10021 93 560270.204178975 113362 6345 10021 93 56028 0.80541485 113362 3863 10021 9356029 −0.208246564 113362 2781 10021 93 56030 0.62596653 113362 199410021 93 56031 −0.038819315 113362 1485 10021 93 56032 0.11029697 1133621146 10021 94 56033 0.013483197 264568 107531 10021 94 56034 0.267608507264568 25598 10021 94 56035 0.416721507 264568 47226 10021 94 560360.655798863 264568 26630 10021 94 56037 0.037821005 264568 17957

TABLE L1 columns 7-14 Centroid variable Source Source Source variableNumber of value pct customers with customers with value pct uniquevalues Source Source of variable variable variable value of variable forvariable allrecs numsources numpersons 0.055971137 108 8 0.074074074 10895779 183 3802 0.034076675 108 5 0.046296296 10 895779 183 38020.02453203 108 2 0.018518519 10 895779 183 3802 0.017589668 108 40.037037037 10 895779 183 3802 0.013099628 108 2 0.018518519 10 895779183 3802 0.010109208 108 1 0.009259259 10 895779 183 3802 0.406439932271 84 0.3099631 10 895779 183 3802 0.096753954 271 45 0.166051661 10895779 183 3802 0.178502313 271 45 0.166051661 10 895779 183 38020.100654652 271 30 0.110701107 10 895779 183 3802 0.06787291 271 200.073800738 10 895779 183 3802

Let CentroidMean (written as CentroidVariableValuePctOfVariable in TableL1) be the average rate of occurrence of a demographic trait in areference population such as the US population. It may be calculated bysumming all persons with the trait divided by all persons using thepopulation sample. Let CentroidStandardDeviation be the averagedivergence in each demographic within the reference population. This maybe calculated by bootstrap sampling the reference population.

1. Assemble the person demographics X into a vector of 0 and 1. If the0s aren't stored for each person, then these can be generated by outerjoining with the list of all available demographics and setting missingdemographics in the person's profile to 0.

2. Assemble the target demographics into a vector of probabilities Y.

3. Scale each vector element by a rarity formula. Below are twoexamples. Log Lifts can be calculated as shown in equations 82 and 83:

X′=log(X/CentroidMean1)  [Equation 82]

Y′=log(Y/CentroidMean2)  [Equation 83]

Z-Scores can be generated as shown in equations 83 and 84:

X′=(X−CentroidMean1)/CentroidStdev1  [Equation 83]

Y′=(Y−CentroidMean2)/CentroidStdev2  [Equation 84]

The CentroidMean and CentroidStdev for reference populations need not bethe same for cable subscribers (X) and buyer-cable subscribers (Y). Insome cases the particular population that these persons come from isvery different and each needs to use a specific reference population tonormalize each.

4. Define a weight for each vector element by a demographicvariable-value weight. This weight can be calculated as the inverse ofthe number of demographic-variable-values that are present for anydemographic. Or it can be entered based on another analysis of whichdemographics traits carry the most weight.

TABLE M DemographicsWeight. The weights above can be developedseparately based on an analysis of how well each contributes toprediction. A simple weighting scheme may also be to set each weight to(1/number of demographic values). Demographic Variable Weight Pets 1 NewVehicled 0.5 Used Vehicle 0.5 New Parent - Child Less than 6 Months0.333333333 New Parent - Child 7-9 Months 0.333333333 New Parent - Child10-12 Months 0.333333333 Expectant Parent 0.5 Intends to Purchase aVehicle 0.333333333

5. Calculate the vector match between the individual vector and thetarget vector, using one of several methods.

Correlation: The degree-of-match could be calculated using a weightedcorrelation calculation shown in Equation 85 and 86.

$\begin{matrix}{{{E\lbrack x\rbrack} = {\Sigma_{i}\mspace{14mu} w_{i}x_{i}}};{{E\lbrack y\rbrack} = {\Sigma_{i}\mspace{14mu} w_{i}y_{i}}}} & \left\lbrack {{Equation}\mspace{14mu} 85} \right\rbrack \\{{r_{\Omega}\left( {X,Y} \right)} = \frac{\Sigma_{i}\mspace{14mu} {w_{i}\left( {x_{i} - {E\lbrack x\rbrack}} \right)}\left( {y_{i} - {E\lbrack y\rbrack}} \right)}{\sqrt{\Sigma_{i}\mspace{14mu} {w_{i}\left( {x_{i} - {E\lbrack x\rbrack}} \right)}^{2}\left( {y_{i} - {E\lbrack y\rbrack}} \right)^{2}}}} & \left\lbrack {{Equation}\mspace{14mu} 86} \right\rbrack\end{matrix}$

Inverse Euclidean Distance: Variants of Euclidean distance can also beused to measure the vector similarity. This is shown in Equation 87.

r _(Ω)(X,Y)×(Σ_(i) w _(i)(x _(i) −y _(i))²)^(−1/2)  [Equation 87]

An example SQL PROC call to execute this procedure is as follows:

-   -   exec Person.Scoring <PopulationToScore>,<TargetDefinition>

where <PopulationToScore> is a sourcekey and <TargetDefinition> is alsoa sourcekey. An example of the above call is below:

-   -   exec Baker.Acxiom.Person.Scoring ‘110424’,‘110402’

In the above example, 110424 is a population of insurance customers.They are each scored against target 110402-NC-1 (natural cluster 1).

An example SQL Query to view results is shown below:

-   -   select top 10 * from Person.SourceTargetTratio where        sourcesegmentkey=‘110424’ and        sourcesegmentkeytarget=‘110402-NC--1’

An example output from the algorithm is shown in Table A and Table N.

TABLE N Excerpt from Addressable algorithm output showing the columnsgenerated for illustrative purposes. Another example output with morecolumns is shown in Table A. Source Source Segment Person Segment KeyTarget Key Key TRatio 110402-NC-1 10532089 110424 0.139791 110402-NC-110532146 110424 0.0143 110402-NC-1 10532158 110424 0.012744 110402-NC--110532187 110424 0.202012 110402-NC--1 10532395 110424 0.194542110402-NC--1 10532513 110424 −0.36149 110402-NC--1 10532573 1104240.154197 110402-NC--1 10532580 110424 0.138061 110402-NC--1 10532674110424 0.076452 110402-NC--1 10532713 110424 −0.00053

The system described above has good scaling characteristics, and so maybe deployed on the higher data volumes available with set top boxes anddemographics. Given S=20 million set top boxes, M=60 million cablesubscribers, D=3,000 demographics per person, and C=1 buyer profiletargets, the training time for calculating matches can be calculated inM*S*D+C*S*D+C*M*D time, which is linear in M, set top boxes, anddemographics, so may be highly efficient.

The algorithm can also be implemented in a way that avoids transmissionof personal information between companies—which is useful for privacyreasons. The above algorithm could be implemented by having productpurchaser information and cable subscriber information copied to alocation, and then aggregates the purchaser information and calculatematch to cable subscribers. However a technique that avoids transmittingthe product purchaser data and cable subscriber “over the wire” is toaggregate product purchasers into an anonymous aggregated demographicvector. This can be executed by the advertiser—for example—or the entitywhich has the primary relationship with the product purchasers. Theanonymous, aggregated, vector—without the persons—can then be passed tothe ad targeting entity—which may be a cable operator who has a directrelationship with the cable subscribers. The match to each cable personcan then be calculated. Therefore cable subscriber information is notdisseminated outside of the cable company, nor is product purchaserinformation disseminated outside of the advertiser.

The invention can also provide detailed information on “why” particularcable subscribers—or packages of subscribers—are scoring highly, or inwhat respects these persons are a match for the advertiser. The reasonwhy a cable subscriber—or a package of cable subscribers—received a highscore, can be shown by calculating the covariance of each demographicd,v between product purchasers and cable subscriber or group of cablesubscribers (250):

Cov=( P _(d,v) ⁺ ·M _(i,d,v) ⁺)  [Equation 10]

The co-variances for each demographic can then be sorted from highest tolowest (255). This will show the demographics d,v with the highestcovariance between the buying population P and media viewing populationM _(i). This may provide insight into what is driving the match, and mayhelp media buyers understand why the media matches their target as shownin FIG. 4. shows an example comparison between product purchaserdemographic and cable subscriber demographics, revealing that the cablesubscriber population is a good match is because the advertiser islooking for low income, retired, 65+ people, and the cable subscribermatches these criteria.

In FIG. 4, the wider bars refer to the standardized demographic scorefor the program. The narrower bars are the standardized demographicreading for the advertiser's target.

V. Addressable Algorithm Output

The output from the addressable algorithms (C or D) is a set ofpersonkeys with tratio. Table A shows an example of this output.

Table A shows an example of the output generated from a call such as“exec Person.Scoring ‘110424’,‘110402’”. The column namedSourceSegmentKey is the population of persons that we scored (110424),SourceSegmentKeyTarget (110402) is an identifier for the population ofproduct purchasers who were the target that we scored against, PersonKeyis a unique key representing a cable subscriber, VariableValueCounts arethe number of demographics that this person had which were non-zero andwhich matched the corresponding demographic in theSourceSegmentKeyTarget demographic profile, tratio is the calculatedcorrelation coefficient—we weight demographic vector elementsdifferently and so this is a weighted correlation, tratioUnweighted iscorrelation coefficient unweighted, tratiounweightedpositive is thecorrelation coefficient only using demographics that were present (thisis computationally much more efficient, e.g. if there are on average 300present demographics and 3000 total possible, then this would drop theoperations by 10× or more, however it is also inaccurate), Unweighteddistance is the Euclidean distance between the cable subscriber vectorand the product purchaser vector, Dist is the Euclidean distance withweights on particular demographics, and ClusterID is the cluster towhich the personkey belongs. A Cluster is a segment or grouping. In thiscase it was generated using a K-means process. The cluster results canbe seen in FIG. 10 where the lift for different clusters is reported.

TABLE A columns 1-7 Source Segment Source Variable TRatio Un- Key PersonSegment Value TRatio Un- weighted Target Key Key Counts TRatio WeightedPositive 110402 19298389 110424 220 0.385928 0.204713 0.127249 11040230868985 110424 199 0.365495 0.245077 0.157574 110402 19174232 110424233 0.364809 0.21583 0.147793 110402 19531659 110424 233 0.3641970.240444 0.173181 110402 19532235 110424 226 0.360298 0.184925 0.183636110402 19054857 110424 213 0.360219 0.230345 0.188773 110402 19174076110424 217 0.359639 0.212313 0.133903 110402 32219451 110424 1960.358918 0.212269 0.212729 110402 19085053 110424 224 0.35824 0.1925990.184416 Corr Un- Un- TRatio weighted Corr Weighted Dist ClusterPositive Prob Prob Dist Dist Prob ID 0.226913 0.626077 0.684781 556.318937.3734 0.230774 2 0.233954 0.623585 0.671212 596.4309 916.22290.231763 1 0.207034 0.641075 0.73472 442.2053 674.0267 0.17402 30.277324 0.686021 0.727547 428.6808 679.9121 0.181987 3 0.29449 0.6398970.702935 571.5545 920.0677 0.219922 3 0.287615 0.644281 0.675603401.6007 598.5678 0.167337 3 0.217374 0.62326 0.688755 548.1325 769.85270.199459 2 0.313002 0.740417 0.750409 539.3746 789.5785 0.182453 20.286724 0.62383 0.675448 424.8026 692.2451 0.18176 3

TABLE A columns 8-14: Addressable Algorithm Output. Ranking SegmentRanking Source Source Source Key Person Key Create Date Value RatingSegment Segment 3 Segment 4 110402 19035090 Dec. 5, 2012 10:04 PM 32Direct Bill 110402 19035102 Dec. 5, 2012 10:04 PM 20 ABW 110402 19035112Dec. 5, 2012 10:04 PM 17 Direct Bill 110402 19035154 Dec. 5, 2012 10:04PM 45 Direct Bill 110402 19035285 Dec. 5, 2012 10:04 PM 10 Direct Bill110402 19035313 Dec. 5, 2012 10:04 PM 26 ABW 110402 19035364 Dec. 5,2012 10:04 PM 21 ABW 110402 19035417 Dec. 5, 2012 10:04 PM 37 ABW 11040219035419 Dec. 5, 2012 10:04 PM 29 ABW 110402 19035420 Dec. 5, 2012 10:04PM 32 Direct Bill

TABLE B columns 1-8 Variable Source Source Cluster Acquisition CustomerValue Segment5 Segment6 ID Value1 Date ChurnDate ID TRatio Counts 1 0 2567.42 NULL NULL A 0.496536 21 1 0 1 1113.08 NULL NULL B 0.300848 33 1 12 556.24 NULL NULL C 0.247989 70 0 0 3 1664.86 NULL NULL D 0.369883 93 00 3 844.34 NULL NULL E 0.67686 11 1 0 2 914.97 NULL NULL F 0.249641 52 10 3 1689.21 NULL NULL G 0.385853 24 1 0 3 1137.11 NULL NULL H 0.08898183 1 1 2 2062.05 NULL NULL I 0.42078 25 0 0 3 1732.96 NULL NULL J0.235426 45 Table B columns 9-17: PersonSource table. This tablecontains a list of persons who are part of a population in this casekeyed by 110402. Persons have a unique PersonKey. Each person carriesadvertiser-assigned value and segment information - for example,PersonKey = 19035090 has an advertiser-provided revenue of 562, andbelongs to a segment called “Direct Bill.” The column tratio has theindividual person tratio as calculated by the addressable algorithm. Theabove data can be used to measure the addressable algorithm's lift.

VI. Lift Measurement of Addressable Targeting Using a Known AdvertiserPopulation

We next describe a process where the lift from the addressabletelevision algorithm is estimated. The process below refers to anexemplary television advertiser, e.g., advertiser 110402, who had runover 20,000 airings and was selling a life insurance product was used.The exemplary television advertiser set aside 50,000 known buyers intheir population that were used as a target population. FollowingAlgorithm D, the target population of product purchasers were enrichedwith demographics and used to generate a target profile. The targetdemographic profile is shown in FIG. 8. The customers tended to be 60+,retired, low income, rural, and likely to own a compact car.

In FIG. 8, variables are indexed compared to U.S. population, so forexample, 2.0 means that twice as many persons who have bought theadvertiser's product have this trait than what would be expected if arandom sample of persons were collected from the U.S. population.

The advertiser also had another 800,000 different customers who had alsoheld policies with their company. These customers would be treated likemake-believe “cable subscribers” so as to be able to generate liftmeasurements. This population carries with it value information assignedby the advertiser. The value information can be a unique performancemeasure for the advertiser, and in this example we have used revenue andhow long they had held policies—so we could see how valuable each ofthese persons is to the advertiser in reality. Next this population wasscored using Algorithm D. After scoring each person had an associatedtratio.

Each of these persons included a known advertiser-assigned-valueincluding revenue accrued from their policies (“revenue”), and number ofmonths they had retained their life insurance policy (“months inforce”).

FIGS. 9 and 10 show the population ordered by target score (tratio), andtheir expected policy durations and revenue.

As shown in FIG. 9, as targeting score increases, so does the expectedrevenue. Also shown in the diagram are three natural clusters(NC1,NC2,NC3) inferred using the k-means algorithm on the demographicdata from each person. These are sub-populations: NC1 tend to be oldermales, NC3 older females, and NC2 tend to be younger persons. NC2 is notas good a target as the others and is less valuable, and this is clearlyshown in FIGS. 9 and 10 as it is shifted lower than the other two. Asthe clusters increase their targeting score, the persons being targetedeach become more valuable. This relationship holds across clusters andoverall population.

As shown in FIG. 10, as targeting score increases, the probability ofholding policies for longer periods of time increases. This report showsthat people who were closer to the demographic profile for buyers tendedto have longer retention on policies and higher revenue.

Lift could then be calculated as the customer value (e.g. months inforce or revenue in this example) divided by the average customer valuein the population.

Another calculation method is to calculate lift as the customer valuedivided by the average customer value from a subset of customers who areconsidered to be representative of the customers who are found in the USPopulation. This would provide an estimate of the lift from addressabletargeting compared to random ads on television.

VII. Lift Measurement of Addressable Targeting Algorithms Compared toSpot Television Algorithms

It is also possible to measure the lift between buying AddressableTelevision media (scored by Algorithm C or D), versus buyingConventional television media (scored by algorithm A, B or other spotalgorithms that produce a score for spot media).

This comparison can help to identify the relative value of addressabletelevision ads compared to traditional spot television ads as well asthe relative performance of different algorithms. For example, nationaltelevision media might have an average CPM of $6. Using the techniquedescribed next, we may find that the average lift (for the top 1% oftraditional spot inventory that is available to purchase) might be 2.0.We might next calculate that addressable targeting can confer a lift of10.0 (for a package of the top 1% of cable subscribers as scored by theaddressable Algorithm D). In this case we would be able to note thataddressable targeting has 5 times the lift of a conventional televisionspot buy. If the advertiser is able to—or had been buying—theconventional spot at $6 and found that their television buy wasprofitable, they should also be able to buy the addressable inventory ata CPM of $30 and be profitable.

This technique of (a) estimating lift due to addressable televisioninventory, (b) estimating lift due to conventional television spots, (c)calculating the ratio of the two lifts, and (d) using the ratio to helpset a CPM that appropriately values the improved lift due toaddressable, is extremely useful both for (i) advertisers, who want tocalculate whether or not addressable media at a given CPM will be costeffective for them to buy, and also (ii) cable and satellite companieswho are selling addressable media, as they want to be able to rationallyset prices for their addressable inventory. Pricing is currently a majorproblem with addressable inventory and we will describe this applicationin more detail next:

In order to measure advertiser lift, we need a measure of the value ofthe media that is relevant to the advertiser, and which is as universalas possible—ie. that could be applicable to—and calculable for—any ofthe many advertisers who buy television media. We define our universalmeasure of value as based on the number of buyers per total audience whoare in the media asset being purchased.

The above definition can be calculated for both convention televisionspots as well as addressable inventory: (1) For addressable inventory,Buyers are cable subscribers who are known to have purchased theadvertiser's product before, and the quality of targeting is the cablesubscribers who are buyers divided by total subscribers who have beentargeted.

(2) For conventional television spots, the quality of targeting may bemeasured by buyers in the audience of program divided by total viewerswatching the program.

“Buyers,” in both cases, are identified by matching advertiser knownpurchasers to cable subscriber/viewers. One algorithmic method ofmatching is to use a third party company takes name-address informationand attaches an anonymous Identifier to person records, and then stripsthe personally identifiable information and sends it back. The sameservice is then used for both product purchasers, and cable subscribers.Thus we then have a set of product purchasers with a universalidentifier, a set of cable subscribers with universal identifier, andthen we can select out cable subscribers who are exact matches toproduct purchasers. Companies including Acxiom and Experian currentlyoffer this anonymization and universalID tagging service.

Buyers per impression therefore can be used to measure the value ofeither addressable packages, or conventional media audiences. A majorbenefit of this calculation is that it is not impacted by a variety offactors that can obscure estimates of quality of targeting. For example,often advertisers run very long television campaigns (e.g. companiessuch as Kraft might have been advertising for 10 years), and this canresult in “media fatigue” where the performance of television declinesover time due to repeated re-exposure. Another example is the particulartelevision commercial creative used, may induce it to be more effectiveor less effective—for example, Creative A may be more intrinsicallyeffective than Creative B. If there are more Creative As being run onparticular networks, then the estimate of quality of targeting on thosenetworks would be artificially high. Buyers per impression or Buyers permillion is a universal measure for how well the ad is reaching the highprobability buyers—i.e., how well the ad is being targeted, and is notaffected by these specific factors about the ad (these factors are ofinterest when measuring the total effectiveness of an advertisingcampaign, but even if the objective is to measure total effectiveness,it is useful to be able to independently measure the quality oftargeting without other factors interfering with it).

Buyers per million is affected by the propensity of the population tobuy the product in question, and as a result if it is un-normalized,this metric is directly comparable for advertisers in the same industryselling similar products. In order to compare across industries, themetric has to either be normalized by rate of purchase, oralternatively, an equivalent number of seed purchaser-cable subscribersshould be selected, so that the average Pr(Buyer) is equivalent betweenproducts. The lookalike method described later can be used to generatethat equivalently-sized pool of pseudo-product-purchaser persons,resulting in a buyer per million metric that is comparable acrossindustries and products (specifically we set the purchaser-cablesubscriber number to the same number for different advertisers, and thenuse lookalikes to add persons, or remove persons with the lowest tratiosuntil we reach the set number of persons.

For conventional television ad spots, cable and broadcast media may berepresented as network-day-hours during one week. There areapproximately 35,000 buyable Networks-Day-Hours, and an example of thismedia definition could be CNN-Tues-8 pm. For Addressable televisioninventory, the inventory may be represented as cable subscribers, and sothe cardinality is equal to the cable subscriber universe; for example,a small cable operator might have a universe of 4 million cablesubscribers who could each potentially be targeted, or a large one likeComcast might have 60 million subscribers. An example of an addressableasset in this case is Cable Subscriber (Person)=1234.

Thus, there are 35,000 spot placements, which may be scored usingconventional algorithms (M32, TRP; Algorithms B, A). There is also acable population where each individual person may be scored usingAddressable Algorithm D or C, and for example, DirecTV may have 20million subscribers who can be scored.

Each algorithm will score its media. We can now perform a variety ofanalyses to measure lift.

In one example, we may rank the media in order of the algorithm score(e.g. tratio), and examine the media that has been ranked, and measurethe actual buyers per million in that media. This enables us tocalculate lift for each algorithm as the buyers per million divided bypopulation average buyers per million. FIG. 11, 12, 13 show exampleanalyses. FIG. 11 is a Cumulative Buyers isolated given percentile ofcases. FIG. 12 shows the expected buyers per million at different tratiolevels, given that tratio has been broken into percentiles. FIG. 13shows lift compared to random targeting versus percentile ofpopulation—when sorted into order by tratio largest to smallest. Usingthese analyses it is possible to compare different algorithms againsttheir ability to find high buyer per million assets.

Addressable Inventory Price Calculation Using BPM Lift Measurement

Given two packages of media, {M_(R) ⁺} and {M_(A) ⁺}, where the first israndomly assembled, and the second is selected by Algorithm A, we cancalculate lift as follows by comparing the ratio of their Buyer permillion counts:

$\begin{matrix}{{{Lift}\left( {\left\{ M_{A}^{+} \right\},\left\{ M_{R}^{+} \right\}} \right)} = \frac{{BPM}\left( \left\{ M_{A}^{+} \right\} \right)}{{BPM}\left( \left\{ M_{R}^{+} \right\} \right)}} & \left\lbrack {{Equation}\mspace{14mu} 88} \right\rbrack\end{matrix}$

Let us now suppose that we have two Targeting Algorithms: A—whichoperates on traditional television spot inventory—and D—which is anaddressable algorithm and operates on addressable inventory. We cancalculate each of their lifts using Equation 88.

The ratio of Equation 88 calculated for Algorithm A and Algorithm D isthe RelativeLlift between Algorithm A and D.

$\begin{matrix}{{{RelativeLift}\left( {\left\{ M_{D}^{+} \right\},\left\{ M_{A}^{+} \right\}} \right)} = \frac{{Lift}\left( {\left\{ M_{D}^{+} \right\},\left\{ M_{R}^{+} \right\}} \right)}{{Lift}\left( {\left\{ M_{A}^{+} \right\},\left\{ M_{R}^{+} \right\}} \right)}} & \left\lbrack {{Equation}\mspace{14mu} 89} \right\rbrack\end{matrix}$

Let us also say that we have a price for traditional television spotmedia CPM({M_(A) ⁺}) that is accepted as a reasonable “rate card price”for media.

A price for Addressable media can now be calculated as follows:

CPM({M _(D) ⁺})=CPM({M _(A) ⁺})*RelativeLift({M _(D) ⁺ },{M _(A)⁺})  [Equation 90]

This price appropriately increases the CPM price based on the relativerichness of buyers per million that are able to be isolated withAlgorithm D versus Algorithm A.

Setting prices on addressable inventory by algorithms including (a)calculating the price based on a function of the buyers per million inthe media, and/or (b) using a known rate-card price on traditional mediaand then scaling to an addressable price based on knowledge of therelative richness of buyers; provides for a method of improved pricesetting on addressable media. For example, if CPM({M_(A) ⁺}) is known tobe profitable or cost effective for an advertiser, then Equation 90 willhelp to set the price of addressable media such that it is alsoprofitable or cost effective for the same advertiser. This can help toensure that prices on Addressable media packages are rational. Aproperty of this pricing algorithm is that smaller audiences beingtargeted, would attract prices that are higher since the lift from thoseaudiences is higher.

The above calculations can be modified in several ways. When calculatingthe number of Buyers, often direct matches between the advertiser'shistorical customers and cable subscribers is very low. For example, andadvertiser might only have 2,000 product purchasers. When this happens,there might be only 1 purchaser-cable subscriber detected, or perhapsnone at all. In addition, even if there are some matches, the resultingprofile might be dominated by the peculiarities of that tiny number ofmatching people, and some of the demographics may be spurious due to thelow number of people in the same (for example, perhaps there is adiabetes person in the group—that could push the rate of diabetesunusually high and cause matches to drive off that variable). As aresult, lookalike matches may be performed to increase the number ofpersons who are used as the “seed population” for building the targetprofile, and thereby increase the robustness of the vector being usedfor matching. “Lookalike matching” works by taking the set of knownproduct purchasers, enriching them with demographics. The algorithm thenfinds non-purchaser cable subscribers who have the most similardemographics to the purchasers. The number of look-a-likes can bedefined by a fixed number (e.g. 10,000 total purchaser-cable subscribersplus lookalikes) or another criteria (e.g. find look-a-likes with scoresbetter than x). The look-a-like cable subscribers plus purchaser-cablesubscribers (if any) are then used as the set of purchaser-cablesubscribers.

“Lookalike matching” can be performed even if there are no directmatches at all, since fundamentally the demographic profile of theproduct purchasers is used to identify lookalike cable subscribers.

Another modification may be to use target definitions for advertisingindustries, rather than specific advertisers. There are tens ofthousands of television advertisers, and many may not have good productpurchaser data. However if there are at least some advertiser's withproduct purchasers, then these can be used as the canonical targetprofiles for industries to which that advertiser belongs. For example,if one life insurance advertiser's product purchasers are known, thenthese persons can be used as a proxy for the life insurance industryoverall. Thus, addressable audience packages can be created forindustries like “Life insurance,” “Investment Services” etc. Theadvertiser can then purchase these dynamically scored packages of cablesubscribers, rather than trying to run the addressable algorithm ontheir own historical customers. In practice this is a usefulmodification that enables the invention to be able to service a widerange of advertisers—and similarly allows prices to be estimated forlarge groups of advertisers.

A. Reports

FIG. 11 shows a lift analysis for Algorithm A, B, and D using advertisercalculated buyer per million data. The chart shows the cumulativepercentage of buyers isolated in media (or subscribers) if that media issorted in order of highest score to lowest. In FIG. 11, an optimalalgorithm would hug the left-hand axis and the diagonal line indicatesrandom performance. That ratio of y-axis-value for algorithm to they-axis value for random is equal to the lift over random. For example,if an advertiser had a budget to target the best scoring 1% of thepopulation, and were using Addressable targeting, their lift would be9.9×. If they targeted ads to 2% of subscribers, their lift would dropto 6.5×. The diagonal line shows the performance of a theoreticalcampaign in which assets are bought randomly.

In the case of this particular advertiser, this analysis shows that TRPs(Algorithm A) are somewhat unreliable and have almost random performancein the first 15% of targetable assets (1.07× for 15%). In contrast, M32(Algorithm B) shows more consistent performance (1.32× in 15%).Addressable Algorithm D achieves 2.42× lift in the top 15%.

The quality of targeting across the full range of targetable assets canbe summarized by a metric known as Area Under the Curve (AUC), whereincreased lift is reflected in a larger AUC. The AUC for AddressableAlgorithm D, M32 and TRPs are 0.668, 0.579 and 0.529, respectively (seeTable 2).

The strength of the relationship between targeting score and buyers perimpression is summarized by the R² Statistic. A greater R² scoreindicates a stronger relationship. Addressable Algorithm D has an R² of0.499 compared to 0.532 for M32 and 0.242 for TRP (Table 2).

FIG. 12 depicts addressable algorithm D performance as buyers perimpression versus tratio. In FIG. 12 the x-axis is tratio divided into100 percentile buckets and y-axis is buyers per impression. Each circlerepresents the number of buyers in each bucket. The flat region fromtratio −0.3 to −0.05 is a common feature in television targetingcurves—this shows that a lot of TV impressions are essentially “wasted”if they are not interested in the product.

FIG. 13 depicts addressable algorithm D lift versus % of assetstargeted, reported in percentiles. Although addressable algorithm Dtargeting lift is high, the key question is how much higher is this liftcompared to what could be achieved by buying audiences in conventionalTV programs and conventional media prices. FIG. 14 attempts to answerthis question by showing the ratio between lift from addressablealgorithm D over M32 (Algorithm B) and TRPs (Algorithm A).

FIG. 14 shows the premium CPM that could be charged for an advertiser bythe % of subscribers that they are targeting. For example, if they aretargeting 10% of subscribers, the publisher could charge 2 times theirstandard national CPM price under addressable algorithm D. Based on thisanalysis—for this example advertiser—it may be concluded that:

1. Peak lift for addressable algorithm D, M32 and TRP are approximately9.9×, 1.75× and 1.21×, respectively.

2. Target size strongly correlates to lift: Lift from addressablealgorithm D may decrease exponentially with target size. At 10% ofpopulation, addressable algorithm D may not be substantially higherperforming than conventional TV media. If more than 32% of householdsmust receive an ad, then addressable targeting may perform worse thanthe best conventional media targeting option (ie. because a lot of poormatching subscribers are being required to be purchased, it is actuallypossible to get a more “pure” audience by buying conventional televisioncommercial break spots on certain programs).

Table 3 shows the comparison of lift between each algorithm. Addr/TRP isthe ratio of lift between Algorithm D and Algorithm A. This shows thataddressable algorithm D would deliver about 7 times better performanceper viewer reached (for the top 1% of addressable cable subscribers),than very selectively buying the top 1% of conventional television spotmedia. Thus the price for the addressable inventory—for a 1% cablesubscriber purchase—could be set to about 7 times higher than that ofthe conventional media.

TABLE 1A Performance for three different TV targeting algorithms MeasureTRP M32 Addr AUC 0.529 0.579 0.668 R² 0.242 0.532 0.499 Lift top 1%1.266 1.749 9.912 Lift top 10% 0.957 1.403 3.177 Lift top 20% 1.1111.317 2.195

TABLE 2 Lift versus % of Assets targeted TRP M32 M32/ Addr Addr/ Addr/Addr/ Pctl lift lift TRP Lift TRP M32 Max M32  0% 1.27 1.75 38% 9.917.83 5.67 5.26  1% 1.53 1.88 23% 6.54 4.26 3.47 3.47  2% 1.37 1.50 10%5.41 3.95 3.60 2.87  3% 1.16 1.36 17% 4.49 3.87 3.31 2.38  4% 1.10 1.3220% 4.13 3.75 3.12 2.19  5% 1.07 1.30 21% 3.73 3.48 2.88 1.98  6% 1.041.29 24% 3.59 3.47 2.79 1.91  7% 1.00 1.31 32% 3.40 3.40 2.59 1.81  8%0.98 1.39 42% 3.32 3.40 2.40 1.76  9% 0.96 1.40 47% 3.18 3.32 2.26 1.6910% 0.97 1.41 45% 3.01 3.09 2.13 1.60 11% 0.97 1.40 45% 2.83 2.93 2.011.50 12% 1.00 1.39 39% 2.70 2.69 1.94 1.43 13% 1.03 1.37 34% 2.62 2.551.91 1.39 14% 1.07 1.37 27% 2.51 2.34 1.84 1.33 15% 1.07 1.36 27% 2.422.26 1.77 1.28 16% 1.07 1.36 27% 2.34 2.19 1.72 1.24 17% 1.08 1.35 26%2.29 2.13 1.69 1.22 18% 1.10 1.34 22% 2.25 2.05 1.68 1.19 19% 1.11 1.3219% 2.19 1.97 1.67 1.16 20% 1.11 1.30 17% 2.15 1.93 1.65 1.14 21% 1.121.29 15% 2.10 1.87 1.62 1.11 22% 1.12 1.30 17% 2.06 1.85 1.58 1.10 23%1.12 1.31 17% 2.04 1.83 1.56 1.08 24% 1.12 1.30 16% 2.03 1.82 1.56 1.0825% 1.11 1.30 16% 2.01 1.81 1.55 1.07 26% 1.11 1.30 17% 2.00 1.80 1.541.06 27% 1.11 1.30 16% 1.99 1.79 1.54 1.06 28% 1.12 1.30 16% 1.98 1.771.52 1.05 29% 1.12 1.30 17% 1.96 1.75 1.50 1.04 30% 1.11 1.32 18% 1.941.74 1.47 1.03 31% 1.12 1.33 19% 1.92 1.72 1.44 1.02 32% 1.13 1.35 20%1.91 1.69 1.41 1.01 33% 1.14 1.37 20% 1.87 1.65 1.37 0.99 34% 1.13 1.3721% 1.85 1.64 1.35 0.98 35% 1.13 1.38 22% 1.83 1.61 1.32 0.97

TABLE 3 Media prices compared to National. TV Price Divided by TVGeographic Level CPM Households National price National cable 6.6114,000,000 1.0 Local Cable Interconnect 20 500,000 3.0 Zone 40 50,0006.1 Addressable 120 1 18.2

VIII. “Context Addressable” Ad Insertions (“Targeting Algorithm E”)

Addressable television targeting (Algorithms C,D) and conventionaltelevision spot buying (TRP and/or M32) may be combined to create ahigher lift and a better television commercial experience. Traditionalset top boxes generally cannot tell which particular individual of ahousehold is watching TV at any given time. Therefore, knowing that ayoung adult female is in a household is useful, but inserting the ad tothe household with the female occupant, plus into, e.g., Pod A for,e.g., Vampire Diaries, may be a more effective strategy for getting thead in front of the intended target in the household, than an addressabletargeting alone.

In order to combine addressable targeting and buyer targeting a mediainventory may be defined to be a combination of household andconventional media: Mi′=M_(i)×M_(i,addr). Both algorithms Addr and M32may be weighted in targeting the above media. An additional level ofcontextual advertising could be to weight the match between keywords ofthe program and of the advertiser's product.

A. Addressable Market Design and Packaging

When a cable operator sells x % addressable inventory to the advertiserthey are left with (1-x %) inventory that is now left unsold. They needto look for ways to back-fill—either by providing it as a kind of localbreak to other advertisers, or by inserting Public Service Announcements(PSAs). Creative targeting where different creative are delivered todifferent households, is another application that can make use ofaddressable targeting infrastructure, and gets around the target sizeproblem. However the lift possible with this approach is not likely tobe as high as refining the target.

For example, if networks were to sell 1% of targeted households, theincreased value of the inventory mean that the publisher could chargeCPMs up to 5× a conventional media asset price. However, this thenleaves the publisher with 99% of their unsold, and for which they needto find another buyer lest they incur a 95% loss.

In order avoid this problem, some publishers have created minimums onaddressable households x % and price charged per thousand subscribers(CPM). Unfortunately, these minimums may to make it impossible foradvertisers to achieve their needed economics. Current addressabletargeting pilot implementations have a requirement of 20% minimum numberof targeted households, at $120 CPM. At 20% of households, and using ourcalculated lifts (previous section) addressable inventory is only 14%higher performing than standard network-program-dayparts. However theCPM being charged for addressable inventory is 18.2 times higher thanstandard national media prices (Table 4).

Thus Addressable media is currently about 18 times lower performing perdollar compared to a conventional media buy.

In order to solve this problem, publishers could set up an exchange sothat as addressable cable subscriber ad inventory is sold, the remaindermay be easily purchased by other advertisers. The key difference withthis market mechanism is that batches of people would be sold by cableoperators. A market design following these principles is illustrated inFIG. 15.

For any advertiser's target, or a common industry target (such as lifeinsurance), a “package” of viewers may be defined by ranking scoredcable subscribers in order of match score using addressable algorithm Cor D (1510). Then, the viewers may be broken into N packages, forexample, N=100 means there would be a buyable package of viewers whorepresent the top 1% of viewers, the top 2%, and so on (1520).

FIG. 16 depicts buyable assets as a GUI according to exemplaryembodiments of the present disclosure to allow advertisers to targettheir assets.

FIG. 16 depicts a zoom out in an audience planner GUI according toexemplary embodiments of the present disclosure showing the differentassets that could be purchased, bucketed by tratio. In FIG. 16, thex-axis is the tratio. In FIG. 16, each square in the column of squaresmay represent a buyable asset. The higher tratio assets may be websitesor digital segments—it may be possible to get a more “pure” targetaudience with these assets, although they may also be much smaller thanTV programs. TV programs may occupy many of the lower tratio buckets.

As shown in FIG. 16, buyable assets may be histogrammed by degree oftargeting. A mix of TV programs, web sites, and VOD (1610) may bebroadly targeted while VOD, web site and digital segments (1620, 1630and 1640) may be very highly targeted.

FIG. 17 depicts a zoom in detail of an audience planner GUI, such as theGUI depicted in FIG. 16. Zoom in may make it easier to see the differenticons being used to represent each asset. For example, the “WWW” iconmay represent a website, and the “People” icon may represent a digitalsegment. “Insp” may represent the “Inspiration network”—a televisionnetwork that runs religious programming. Addressable audiences may berepresented by a “Play button” icon, as well as familiar TV programs

As shown in FIG. 17, each buyable asset may include, for example, TVprograms (1710), web sites (1720), digital segments (1730), and VOD(1740), etc.

FIG. 18 shows a set of buyable media including TV program 1810, digitalsegment 1830, website 1820, and so on. “Insertable VOD (Insertable VideoOn Demand) 16% of Pop” (1840) is a viewer package that can be purchasedthat will capture the top 16% of population who are most likely toconvert on the advertiser's product.

If the advertiser or sell-side entity has access to the match betweenbuyers and set top box viewers, they may also calculate expected liftestimates using the methods discussed above for selected packages ofranked cable subscribers (1530-1540) by measuring the concentration ofbuyers in the population being purchased. This may help to measure thevalue of the viewership package, and so may help the advertiser todetermine what price to offer. The sell-side entity may then sell theselected packages to the advertiser (1550).

The sell-side entity may then repeat the process (1560) by re-rankingthe remaining viewers who have not been sold using their scores forAddressable Algorithm C or D, and re-forming dynamic packages foradvertisers. There may be thousands of advertisers, and they may eachhave a view into their own unique, dynamic packages that they canpurchase. In this way the sell-side entity may encourage participationby multiple advertisers.

The above auction may be modified in many ways. For example, anotheradvertiser may be allowed to out-bid an earlier advertiser for viewerswho were already auctioned. Novel parts of this embodiment may include,for example, (a) defining packages of buyable media as groups of viewersbased on a ranking of their targeting for a particular advertiser—oradvertiser industry, and (b) auctioning these assets, etc.

Other embodiments of the disclosure will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the invention being indicated by the following claims.

1-20. (canceled)
 21. A method of targeting of advertising content for aconsumer product, the method comprising: calculating, by a hardwareprocessor, a vector of probabilities that a product purchaser deviceamong a plurality of product purchaser devices will have a demographicattribute among a plurality of demographic attributes; calculating, bythe hardware processor, a vector of demographic attributes for eachelectronic content subscriber device among a plurality of electroniccontent subscriber devices; calculating, by the hardware processor, avector match between the vector of probabilities and the vector ofdemographic attributes for each electronic content subscriber deviceamong the plurality of electronic content subscriber devices; andselecting, as target electronic content subscriber devices for theadvertising content, electronic content subscriber devices among theplurality of electronic content subscriber devices based on thecalculated vector match between the vector of probabilities and thevector of demographic attributes for each electronic content subscriberdevice.
 22. The method of claim 21, wherein the vector match iscalculated based on a correlation between the demographic attributes ofthe electronic content subscriber devices and the calculated vector ofprobabilities.
 23. The method of claim 21, further comprising:obtaining, from a set top box set, top box data including viewingbehavior data of a plurality of viewing persons; selecting, aspurchaser-viewers, product purchasers among the plurality of productpurchasers matching viewing persons among the plurality of viewingpersons; selecting electronic content subscriber devices among theplurality of electronic content subscriber devices matching viewingpersons among the plurality of viewing persons; and calculating asimilarity between the product purchaser data and each electroniccontent subscriber device among the plurality of electronic contentsubscriber devices based on viewing behavior data of thepurchaser-viewers and viewing behavior data of the selected electroniccontent subscriber devices.
 24. The method of claim 21, furthercomprising: obtaining, over the network, viewing data of a plurality ofviewing persons, the viewing data including a plurality of viewed mediaviewed by a respective viewing person among the plurality of viewingpersons and the viewed media including attributes of the respectiveviewing person; calculating, by the hardware processor, a similaritybetween one or more product purchasers and each viewed media, the one ormore product purchasers and each viewed media having at least oneattribute in common; selecting viewed media among the viewed media basedon the calculated similarity to the product purchasers as purchasermedia; selecting purchaser media as target media for the advertisingcontent when it is determined that the purchaser media is viewed by oneor more target electronic content subscriber devices.
 25. A system fortargeting of advertising content for a consumer product, the systemcomprising: a server providing consumer demographic data from over thenetwork; an advertising targeting controller configured to: calculate,by a hardware processor, a vector of probabilities that a productpurchaser device among a plurality of product purchaser devices willhave a demographic attribute among a plurality of demographicattributes, calculate, by the hardware processor, a vector ofdemographic attributes for each electronic content subscriber deviceamong a plurality of electronic content subscriber devices; calculate,by the hardware processor, a vector match between the vector ofprobabilities and the vector of demographic attributes for eachelectronic content subscriber device among the plurality of electroniccontent subscriber devices; and select, as target electronic contentsubscriber devices for the advertising content, electronic contentsubscriber devices among the plurality of electronic content subscriberdevices based on the calculated vector match between the vector ofprobabilities and the vector of demographic attributes for eachelectronic content subscriber device.
 26. The system of claim 25,wherein the vector match is calculated based on a correlation betweenthe demographic attributes of the electronic content subscriber devicesand the calculated vector of probabilities.
 27. The system of claim 25,further comprising: a set top box set providing top box data includingviewing behavior data of a plurality of viewing persons, wherein theadvertising targeting controller is further configured to: obtain theset top box data; select, as purchaser-viewers, product purchasers amongthe plurality of product purchasers matching viewing persons among theplurality of viewing persons; and select electronic content subscriberdevices among the plurality of electronic content subscriber devicesmatching viewing persons among the plurality of viewing persons; andcalculate a similarity between the product purchaser data and eachelectronic content subscriber device among the plurality of electroniccontent subscriber devices based on viewing behavior data of thepurchaser-viewers and viewing behavior data of the selected electroniccontent subscriber devices.
 28. The system of claim 25, furthercomprising: a second server providing, over the network, viewing data ofa plurality of viewing persons, the viewing data including a pluralityof viewed media viewed by a respective viewing person among theplurality of viewing persons and the viewed media including attributesof the respective viewing person; wherein the advertising targetingcontroller is further configured to: obtain the viewing data; calculate,by the hardware processor, a similarity between one or more productpurchasers and each viewed media, the one or more product purchasers andeach viewed media having at least one attribute in common; select viewedmedia among the viewed media based on the calculated similarity to theproduct purchasers as purchaser media; and select purchaser media astarget media for the advertising content when it is determined that thepurchaser media is viewed by one or more target electronic contentsubscriber devices.
 29. A non-transitory computer readable mediumstoring a program causing a computer to execute a method of targeting ofadvertising content for a consumer product, the method comprising:calculating, by a hardware processor, a vector of probabilities that aproduct purchaser device among a plurality of product purchaser deviceswill have a demographic attribute among a plurality of demographicattributes, calculating, by the hardware processor, a vector ofdemographic attributes for each electronic content subscriber deviceamong a plurality of electronic content subscriber devices; calculating,by the hardware processor, a vector match between the vector ofprobabilities and the vector of demographic attributes for eachelectronic content subscriber device among the plurality of electroniccontent subscriber devices; and selecting, as target electronic contentsubscriber devices for the advertising content, electronic contentsubscriber devices among the plurality of cable subscriber devices basedon the calculated vector match between the vector of probabilities andthe vector of demographic attributes for each electronic contentsubscriber device.
 30. The non-transitory computer readable mediumaccording to claim 29, wherein the vector match is calculated based on acorrelation between the demographic attributes of the electronic contentsubscriber devices and the calculated vector of probabilities.
 31. Thenon-transitory computer readable medium according to claim 29, theexecuted method further comprising: obtaining, from a set top box, settop box data including viewing behavior data of a plurality of viewingpersons; selecting, as purchaser-viewers, product purchasers among theplurality of product purchasers matching viewing persons among theplurality of viewing persons; selecting electronic content subscriberdevices among the plurality of electronic content subscriber devicesmatching viewing persons among the plurality of viewing persons; andcalculating a similarity between the product purchaser data and eachelectronic content subscriber device among the plurality of electroniccontent subscriber devices based on viewing behavior data of thepurchaser-viewers and viewing behavior data of the selected electroniccontent subscriber devices.
 32. The non-transitory computer readablemedium according to claim 29, the executed method further comprising:obtaining, over the network, viewing data of a plurality of viewingpersons, the viewing data including a plurality of viewed media viewedby a respective viewing person among the plurality of viewing personsand the viewed media including attributes of the respective viewingperson; calculating, by the hardware processor, a similarity between oneor more product purchasers and each viewed media, the one or moreproduct purchasers and each viewed media having at least one attributein common; selecting viewed media among the viewed media based on thecalculated similarity to the product purchasers as purchaser media; andselecting purchaser media as target media for the advertising contentwhen it is determined that the purchaser media is viewed by one or moretarget electronic content subscriber devices.